Mobile Price Project Using Classification Algorithms ( Decission Trees, Random Forest, SVM )¶
Import libraries¶
In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
plt.style.use('ggplot')
In [5]:
# show all columns
pd.set_option('display.max_columns', 50)
Import Datasets¶
In [6]:
train_data = pd.read_csv('phone_train.csv')
test_data = pd.read_csv('phone_test.csv')
In [7]:
df_train = pd.DataFrame(train_data)
df_test = pd.DataFrame(test_data)
In [8]:
df_train
Out[8]:
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 842 | 0 | 2.2 | 0 | 1 | 0 | 7 | 0.6 | 188 | 2 | 2 | 20 | 756 | 2549 | 9 | 7 | 19 | 0 | 0 | 1 | 1 |
| 1 | 1021 | 1 | 0.5 | 1 | 0 | 1 | 53 | 0.7 | 136 | 3 | 6 | 905 | 1988 | 2631 | 17 | 3 | 7 | 1 | 1 | 0 | 2 |
| 2 | 563 | 1 | 0.5 | 1 | 2 | 1 | 41 | 0.9 | 145 | 5 | 6 | 1263 | 1716 | 2603 | 11 | 2 | 9 | 1 | 1 | 0 | 2 |
| 3 | 615 | 1 | 2.5 | 0 | 0 | 0 | 10 | 0.8 | 131 | 6 | 9 | 1216 | 1786 | 2769 | 16 | 8 | 11 | 1 | 0 | 0 | 2 |
| 4 | 1821 | 1 | 1.2 | 0 | 13 | 1 | 44 | 0.6 | 141 | 2 | 14 | 1208 | 1212 | 1411 | 8 | 2 | 15 | 1 | 1 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1995 | 794 | 1 | 0.5 | 1 | 0 | 1 | 2 | 0.8 | 106 | 6 | 14 | 1222 | 1890 | 668 | 13 | 4 | 19 | 1 | 1 | 0 | 0 |
| 1996 | 1965 | 1 | 2.6 | 1 | 0 | 0 | 39 | 0.2 | 187 | 4 | 3 | 915 | 1965 | 2032 | 11 | 10 | 16 | 1 | 1 | 1 | 2 |
| 1997 | 1911 | 0 | 0.9 | 1 | 1 | 1 | 36 | 0.7 | 108 | 8 | 3 | 868 | 1632 | 3057 | 9 | 1 | 5 | 1 | 1 | 0 | 3 |
| 1998 | 1512 | 0 | 0.9 | 0 | 4 | 1 | 46 | 0.1 | 145 | 5 | 5 | 336 | 670 | 869 | 18 | 10 | 19 | 1 | 1 | 1 | 0 |
| 1999 | 510 | 1 | 2.0 | 1 | 5 | 1 | 45 | 0.9 | 168 | 6 | 16 | 483 | 754 | 3919 | 19 | 4 | 2 | 1 | 1 | 1 | 3 |
2000 rows × 21 columns
In [9]:
df_test
Out[9]:
| id | battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1043 | 1 | 1.8 | 1 | 14 | 0 | 5 | 0.1 | 193 | 3 | 16 | 226 | 1412 | 3476 | 12 | 7 | 2 | 0 | 1 | 0 |
| 1 | 2 | 841 | 1 | 0.5 | 1 | 4 | 1 | 61 | 0.8 | 191 | 5 | 12 | 746 | 857 | 3895 | 6 | 0 | 7 | 1 | 0 | 0 |
| 2 | 3 | 1807 | 1 | 2.8 | 0 | 1 | 0 | 27 | 0.9 | 186 | 3 | 4 | 1270 | 1366 | 2396 | 17 | 10 | 10 | 0 | 1 | 1 |
| 3 | 4 | 1546 | 0 | 0.5 | 1 | 18 | 1 | 25 | 0.5 | 96 | 8 | 20 | 295 | 1752 | 3893 | 10 | 0 | 7 | 1 | 1 | 0 |
| 4 | 5 | 1434 | 0 | 1.4 | 0 | 11 | 1 | 49 | 0.5 | 108 | 6 | 18 | 749 | 810 | 1773 | 15 | 8 | 7 | 1 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | 996 | 1700 | 1 | 1.9 | 0 | 0 | 1 | 54 | 0.5 | 170 | 7 | 17 | 644 | 913 | 2121 | 14 | 8 | 15 | 1 | 1 | 0 |
| 996 | 997 | 609 | 0 | 1.8 | 1 | 0 | 0 | 13 | 0.9 | 186 | 4 | 2 | 1152 | 1632 | 1933 | 8 | 1 | 19 | 0 | 1 | 1 |
| 997 | 998 | 1185 | 0 | 1.4 | 0 | 1 | 1 | 8 | 0.5 | 80 | 1 | 12 | 477 | 825 | 1223 | 5 | 0 | 14 | 1 | 0 | 0 |
| 998 | 999 | 1533 | 1 | 0.5 | 1 | 0 | 0 | 50 | 0.4 | 171 | 2 | 12 | 38 | 832 | 2509 | 15 | 11 | 6 | 0 | 1 | 0 |
| 999 | 1000 | 1270 | 1 | 0.5 | 0 | 4 | 1 | 35 | 0.1 | 140 | 6 | 19 | 457 | 608 | 2828 | 9 | 2 | 3 | 1 | 0 | 1 |
1000 rows × 21 columns
Preprocesing¶
Checking the similarity of the test data with the train data
In [10]:
df_train.describe()
Out[10]:
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2000.000000 | 2000.0000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 |
| mean | 1238.518500 | 0.4950 | 1.522250 | 0.509500 | 4.309500 | 0.521500 | 32.046500 | 0.501750 | 140.249000 | 4.520500 | 9.916500 | 645.108000 | 1251.515500 | 2124.213000 | 12.306500 | 5.767000 | 11.011000 | 0.761500 | 0.503000 | 0.507000 | 1.500000 |
| std | 439.418206 | 0.5001 | 0.816004 | 0.500035 | 4.341444 | 0.499662 | 18.145715 | 0.288416 | 35.399655 | 2.287837 | 6.064315 | 443.780811 | 432.199447 | 1084.732044 | 4.213245 | 4.356398 | 5.463955 | 0.426273 | 0.500116 | 0.500076 | 1.118314 |
| min | 501.000000 | 0.0000 | 0.500000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.100000 | 80.000000 | 1.000000 | 0.000000 | 0.000000 | 500.000000 | 256.000000 | 5.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 851.750000 | 0.0000 | 0.700000 | 0.000000 | 1.000000 | 0.000000 | 16.000000 | 0.200000 | 109.000000 | 3.000000 | 5.000000 | 282.750000 | 874.750000 | 1207.500000 | 9.000000 | 2.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 0.750000 |
| 50% | 1226.000000 | 0.0000 | 1.500000 | 1.000000 | 3.000000 | 1.000000 | 32.000000 | 0.500000 | 141.000000 | 4.000000 | 10.000000 | 564.000000 | 1247.000000 | 2146.500000 | 12.000000 | 5.000000 | 11.000000 | 1.000000 | 1.000000 | 1.000000 | 1.500000 |
| 75% | 1615.250000 | 1.0000 | 2.200000 | 1.000000 | 7.000000 | 1.000000 | 48.000000 | 0.800000 | 170.000000 | 7.000000 | 15.000000 | 947.250000 | 1633.000000 | 3064.500000 | 16.000000 | 9.000000 | 16.000000 | 1.000000 | 1.000000 | 1.000000 | 2.250000 |
| max | 1998.000000 | 1.0000 | 3.000000 | 1.000000 | 19.000000 | 1.000000 | 64.000000 | 1.000000 | 200.000000 | 8.000000 | 20.000000 | 1960.000000 | 1998.000000 | 3998.000000 | 19.000000 | 18.000000 | 20.000000 | 1.000000 | 1.000000 | 1.000000 | 3.000000 |
In [11]:
df_test.describe()
Out[11]:
| id | battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 |
| mean | 500.500000 | 1248.510000 | 0.516000 | 1.540900 | 0.517000 | 4.593000 | 0.487000 | 33.652000 | 0.517500 | 139.51100 | 4.328000 | 10.054000 | 627.121000 | 1239.774000 | 2138.998000 | 11.995000 | 5.316000 | 11.085000 | 0.756000 | 0.50000 | 0.507000 |
| std | 288.819436 | 432.458227 | 0.499994 | 0.829268 | 0.499961 | 4.463325 | 0.500081 | 18.128694 | 0.280861 | 34.85155 | 2.288155 | 6.095099 | 432.929699 | 439.670981 | 1088.092278 | 4.320607 | 4.240062 | 5.497636 | 0.429708 | 0.50025 | 0.500201 |
| min | 1.000000 | 500.000000 | 0.000000 | 0.500000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.100000 | 80.00000 | 1.000000 | 0.000000 | 0.000000 | 501.000000 | 263.000000 | 5.000000 | 0.000000 | 2.000000 | 0.000000 | 0.00000 | 0.000000 |
| 25% | 250.750000 | 895.000000 | 0.000000 | 0.700000 | 0.000000 | 1.000000 | 0.000000 | 18.000000 | 0.300000 | 109.75000 | 2.000000 | 5.000000 | 263.750000 | 831.750000 | 1237.250000 | 8.000000 | 2.000000 | 6.750000 | 1.000000 | 0.00000 | 0.000000 |
| 50% | 500.500000 | 1246.500000 | 1.000000 | 1.500000 | 1.000000 | 3.000000 | 0.000000 | 34.500000 | 0.500000 | 139.00000 | 4.000000 | 10.000000 | 564.500000 | 1250.000000 | 2153.500000 | 12.000000 | 5.000000 | 11.000000 | 1.000000 | 0.50000 | 1.000000 |
| 75% | 750.250000 | 1629.250000 | 1.000000 | 2.300000 | 1.000000 | 7.000000 | 1.000000 | 49.000000 | 0.800000 | 170.00000 | 6.000000 | 16.000000 | 903.000000 | 1637.750000 | 3065.500000 | 16.000000 | 8.000000 | 16.000000 | 1.000000 | 1.00000 | 1.000000 |
| max | 1000.000000 | 1999.000000 | 1.000000 | 3.000000 | 1.000000 | 19.000000 | 1.000000 | 64.000000 | 1.000000 | 200.00000 | 8.000000 | 20.000000 | 1907.000000 | 1998.000000 | 3989.000000 | 19.000000 | 18.000000 | 20.000000 | 1.000000 | 1.00000 | 1.000000 |
They're similar
Missing values of train dataset
In [12]:
m = df_train.isnull().sum()
if m.all() == 0:
print('Missing values *train dataset* =>>> False')
else:
print('We have missing values in train dataset:\n', m)
Missing values *train dataset* =>>> False
Missing values of test dataset
In [13]:
m = df_test.isnull().sum()
if m.all() == 0:
print('Missing values *test dataset* =>>> False')
else:
print('We have missing values in test dataset:\n', m)
Missing values *test dataset* =>>> False
Visualization of train dataset
In [14]:
df_train.hist(bins=100,figsize=(30,20))
plt.show()
In [15]:
plt.figure(figsize=(15, 10))
sns.heatmap(df_train.corr(numeric_only=[True]), cmap='Reds', cbar=True, annot=True, linewidths=0.5, annot_kws={"size": 8})
plt.title('Correlation Heatmap')
plt.xticks()
plt.xlabel('Features')
plt.ylabel('Features')
plt.show()
In [16]:
plt.figure(figsize=(15,5))
plt.scatter(x=df_train['ram'], y=df_train['price_range'])
plt.title('compare ram vs target',
backgroundcolor='black',color='red',fontsize=15)
plt.xlabel('ram', fontsize = 15)
plt.ylabel('Price range', fontsize=15)
Out[16]:
Text(0, 0.5, 'Price range')
In [17]:
fig = px.scatter(df_train, x='ram', y='price_range',
color='price_range', marginal_y='violin', marginal_x='box')
fig.update_layout(
title='Compare RAM vs Price Range',
title_x=0.5
)
fig.show()
In [18]:
sns.jointplot(x='ram', y='price_range', data=df_train, color='red', kind='kde')
Out[18]:
<seaborn.axisgrid.JointGrid at 0x1f2f0997590>
In [19]:
fig = px.density_contour(df_train, x='ram', y='price_range')
fig.update_layout(
title='Compare RAM vs Price Range',
title_x=0.5,
title_yanchor='top'
)
fig.show()
In [20]:
labels = ['3G-Supported', 'Not Supported']
values = df_train['three_g'].value_counts().values
fig = px.pie(values=values,
names=labels,
hole=.3
)
fig.update_layout(
title_text="3G",
title_x=0.45,
title_yanchor="middle"
)
fig.show()
In [21]:
labels = ['4G-Supported', 'Not Supported']
values = df_train['four_g'].value_counts().values
fig = px.pie(values=values,
names=labels,
hole=.3
)
fig.update_layout(
title_text="4G",
title_x=0.45,
title_yanchor="middle"
)
fig.show()
In [22]:
labels = ['1', '2', '3', '4', '5', '6', '7', '8']
values = df_train['n_cores'].value_counts().values
fig = px.pie(values=values,
names=labels,
hole=0.3
)
fig.update_layout(
title_text="n_cores",
title_x=0.5,
title_yanchor="middle"
)
fig.show()
In [23]:
df_train.describe()
Out[23]:
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2000.000000 | 2000.0000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 | 2000.000000 |
| mean | 1238.518500 | 0.4950 | 1.522250 | 0.509500 | 4.309500 | 0.521500 | 32.046500 | 0.501750 | 140.249000 | 4.520500 | 9.916500 | 645.108000 | 1251.515500 | 2124.213000 | 12.306500 | 5.767000 | 11.011000 | 0.761500 | 0.503000 | 0.507000 | 1.500000 |
| std | 439.418206 | 0.5001 | 0.816004 | 0.500035 | 4.341444 | 0.499662 | 18.145715 | 0.288416 | 35.399655 | 2.287837 | 6.064315 | 443.780811 | 432.199447 | 1084.732044 | 4.213245 | 4.356398 | 5.463955 | 0.426273 | 0.500116 | 0.500076 | 1.118314 |
| min | 501.000000 | 0.0000 | 0.500000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.100000 | 80.000000 | 1.000000 | 0.000000 | 0.000000 | 500.000000 | 256.000000 | 5.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 851.750000 | 0.0000 | 0.700000 | 0.000000 | 1.000000 | 0.000000 | 16.000000 | 0.200000 | 109.000000 | 3.000000 | 5.000000 | 282.750000 | 874.750000 | 1207.500000 | 9.000000 | 2.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 0.750000 |
| 50% | 1226.000000 | 0.0000 | 1.500000 | 1.000000 | 3.000000 | 1.000000 | 32.000000 | 0.500000 | 141.000000 | 4.000000 | 10.000000 | 564.000000 | 1247.000000 | 2146.500000 | 12.000000 | 5.000000 | 11.000000 | 1.000000 | 1.000000 | 1.000000 | 1.500000 |
| 75% | 1615.250000 | 1.0000 | 2.200000 | 1.000000 | 7.000000 | 1.000000 | 48.000000 | 0.800000 | 170.000000 | 7.000000 | 15.000000 | 947.250000 | 1633.000000 | 3064.500000 | 16.000000 | 9.000000 | 16.000000 | 1.000000 | 1.000000 | 1.000000 | 2.250000 |
| max | 1998.000000 | 1.0000 | 3.000000 | 1.000000 | 19.000000 | 1.000000 | 64.000000 | 1.000000 | 200.000000 | 8.000000 | 20.000000 | 1960.000000 | 1998.000000 | 3998.000000 | 19.000000 | 18.000000 | 20.000000 | 1.000000 | 1.000000 | 1.000000 | 3.000000 |
Checking columns for abnormal values
px_height¶
In [24]:
fig = px.scatter(df_train, x='px_height', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')
fig.update_layout(
title='px_height vs Price Range (before)',
title_x=0.5
)
fig.show()
In [25]:
df_train.px_height.describe()
Out[25]:
count 2000.000000 mean 645.108000 std 443.780811 min 0.000000 25% 282.750000 50% 564.000000 75% 947.250000 max 1960.000000 Name: px_height, dtype: float64
In [26]:
print(df_train.px_height.min())
0
In [27]:
# Remove px_hight = 0
df_train = df_train[df_train['px_height']!=0]
df_train.reset_index(inplace=True)
df_train.drop('index', axis=1, inplace=True)
In [28]:
fig = px.scatter(df_train, x='px_height', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')
fig.update_layout(
title='px_height vs Price Range(after)',
title_x=0.5
)
fig.show()
sc_w¶
In [29]:
fig = px.scatter(df_train, x='sc_w', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')
fig.update_layout(
title='sc_w vs Price Range(before)',
title_x=0.5
)
fig.show()
In [30]:
df_train.sc_w.describe()
Out[30]:
count 1998.000000 mean 5.770270 std 4.356633 min 0.000000 25% 2.000000 50% 5.000000 75% 9.000000 max 18.000000 Name: sc_w, dtype: float64
In [31]:
print(df_train.px_height.min())
1
In [32]:
# Remove sc_w =< 2
df_train = df_train[df_train['sc_w'] >= 2]
df_train.reset_index(inplace=True)
df_train.drop('index', axis=1, inplace=True)
In [33]:
fig = px.scatter(df_train, x='sc_w', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')
fig.update_layout(
title='sc_w vs Price Range(after)',
title_x=0.5
)
fig.show()
In [34]:
df_train
Out[34]:
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 842 | 0 | 2.2 | 0 | 1 | 0 | 7 | 0.6 | 188 | 2 | 2 | 20 | 756 | 2549 | 9 | 7 | 19 | 0 | 0 | 1 | 1 |
| 1 | 1021 | 1 | 0.5 | 1 | 0 | 1 | 53 | 0.7 | 136 | 3 | 6 | 905 | 1988 | 2631 | 17 | 3 | 7 | 1 | 1 | 0 | 2 |
| 2 | 563 | 1 | 0.5 | 1 | 2 | 1 | 41 | 0.9 | 145 | 5 | 6 | 1263 | 1716 | 2603 | 11 | 2 | 9 | 1 | 1 | 0 | 2 |
| 3 | 615 | 1 | 2.5 | 0 | 0 | 0 | 10 | 0.8 | 131 | 6 | 9 | 1216 | 1786 | 2769 | 16 | 8 | 11 | 1 | 0 | 0 | 2 |
| 4 | 1821 | 1 | 1.2 | 0 | 13 | 1 | 44 | 0.6 | 141 | 2 | 14 | 1208 | 1212 | 1411 | 8 | 2 | 15 | 1 | 1 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1604 | 858 | 0 | 2.2 | 0 | 1 | 0 | 50 | 0.1 | 84 | 1 | 2 | 528 | 1416 | 3978 | 17 | 16 | 3 | 1 | 1 | 0 | 3 |
| 1605 | 794 | 1 | 0.5 | 1 | 0 | 1 | 2 | 0.8 | 106 | 6 | 14 | 1222 | 1890 | 668 | 13 | 4 | 19 | 1 | 1 | 0 | 0 |
| 1606 | 1965 | 1 | 2.6 | 1 | 0 | 0 | 39 | 0.2 | 187 | 4 | 3 | 915 | 1965 | 2032 | 11 | 10 | 16 | 1 | 1 | 1 | 2 |
| 1607 | 1512 | 0 | 0.9 | 0 | 4 | 1 | 46 | 0.1 | 145 | 5 | 5 | 336 | 670 | 869 | 18 | 10 | 19 | 1 | 1 | 1 | 0 |
| 1608 | 510 | 1 | 2.0 | 1 | 5 | 1 | 45 | 0.9 | 168 | 6 | 16 | 483 | 754 | 3919 | 19 | 4 | 2 | 1 | 1 | 1 | 3 |
1609 rows × 21 columns
Apply this cahnges to the test dataframe
px_height¶
In [35]:
df_test.px_height.describe()
Out[35]:
count 1000.000000 mean 627.121000 std 432.929699 min 0.000000 25% 263.750000 50% 564.500000 75% 903.000000 max 1907.000000 Name: px_height, dtype: float64
In [36]:
print(df_test.px_height.min())
0
In [37]:
# Remove px_hight = 0
df_test = df_test[df_test['px_height']!=0]
df_test.reset_index(inplace=True)
df_test.drop('index', axis=1, inplace=True)
sc_w¶
In [38]:
df_test.sc_w.describe()
Out[38]:
count 998.000000 mean 5.315631 std 4.244245 min 0.000000 25% 2.000000 50% 5.000000 75% 8.000000 max 18.000000 Name: sc_w, dtype: float64
In [39]:
print(df_test.sc_w.min())
0
In [40]:
# Remove sc_w =< 2
df_test = df_test[df_test['sc_w'] >= 2]
df_test.reset_index(inplace=True)
df_test.drop('index', axis=1, inplace=True)
In [41]:
df_test
Out[41]:
| id | battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1043 | 1 | 1.8 | 1 | 14 | 0 | 5 | 0.1 | 193 | 3 | 16 | 226 | 1412 | 3476 | 12 | 7 | 2 | 0 | 1 | 0 |
| 1 | 3 | 1807 | 1 | 2.8 | 0 | 1 | 0 | 27 | 0.9 | 186 | 3 | 4 | 1270 | 1366 | 2396 | 17 | 10 | 10 | 0 | 1 | 1 |
| 2 | 5 | 1434 | 0 | 1.4 | 0 | 11 | 1 | 49 | 0.5 | 108 | 6 | 18 | 749 | 810 | 1773 | 15 | 8 | 7 | 1 | 0 | 1 |
| 3 | 6 | 1464 | 1 | 2.9 | 1 | 5 | 1 | 50 | 0.8 | 198 | 8 | 9 | 569 | 939 | 3506 | 10 | 7 | 3 | 1 | 1 | 1 |
| 4 | 7 | 1718 | 0 | 2.4 | 0 | 1 | 0 | 47 | 1.0 | 156 | 2 | 3 | 1283 | 1374 | 3873 | 14 | 2 | 10 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 782 | 994 | 567 | 1 | 2.7 | 1 | 14 | 1 | 56 | 0.4 | 165 | 8 | 17 | 555 | 1290 | 336 | 7 | 6 | 7 | 1 | 1 | 1 |
| 783 | 995 | 936 | 1 | 1.4 | 1 | 0 | 0 | 46 | 0.8 | 139 | 2 | 0 | 265 | 886 | 684 | 8 | 5 | 12 | 1 | 1 | 1 |
| 784 | 996 | 1700 | 1 | 1.9 | 0 | 0 | 1 | 54 | 0.5 | 170 | 7 | 17 | 644 | 913 | 2121 | 14 | 8 | 15 | 1 | 1 | 0 |
| 785 | 999 | 1533 | 1 | 0.5 | 1 | 0 | 0 | 50 | 0.4 | 171 | 2 | 12 | 38 | 832 | 2509 | 15 | 11 | 6 | 0 | 1 | 0 |
| 786 | 1000 | 1270 | 1 | 0.5 | 0 | 4 | 1 | 35 | 0.1 | 140 | 6 | 19 | 457 | 608 | 2828 | 9 | 2 | 3 | 1 | 0 | 1 |
787 rows × 21 columns
Model¶
DecisionTrees¶
In [42]:
Xd = df_train.drop('price_range', axis=1)
yd = df_train.price_range.values.reshape(-1, 1)
In [43]:
def DT(Xd, yd, test_sizes, max_depths, criterion, splitter):
df_evaluation = pd.DataFrame(columns=['Test_size', 'Max_depth', 'Criterion', 'Splitter', 'Accuracy', 'Score'])
for test_size in test_sizes:
X_train, X_test, y_train, y_test = train_test_split(Xd, yd, test_size=test_size, random_state=0)
for max_depth in max_depths:
for c in criterion:
for s in splitter:
clf = DecisionTreeClassifier(max_depth=max_depth, criterion=c, splitter=s)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = metrics.accuracy_score(y_test, y_pred)
score = clf.score(Xd, yd)
df_evaluation = pd.concat([df_evaluation, pd.DataFrame({'Test_size': test_size,
'Max_depth': max_depth,
'Criterion': c,
'Splitter': s,
'Accuracy': accuracy,
'Score': score}, index=[0])], ignore_index=True)
return (df_evaluation)
def highlight_max(s):
is_max = s == s.max()
return ['background-color: lightgreen' if v else '' for v in is_max]
test_sizes = [0.1, 0.15, 0.2, 0.25, 0.3]
max_depths = list(range(1, 21))
criterion = ['gini', 'entropy', 'log_loss']
splitter = ['best', 'random']
df_evaluation = DT(Xd, yd, test_sizes, max_depths, criterion=criterion, splitter=splitter)
df_evaluation.style.apply(highlight_max)
Out[43]:
| Test_size | Max_depth | Criterion | Splitter | Accuracy | Score | |
|---|---|---|---|---|---|---|
| 0 | 0.100000 | 1 | gini | best | 0.521739 | 0.497203 |
| 1 | 0.100000 | 1 | gini | random | 0.409938 | 0.442511 |
| 2 | 0.100000 | 1 | entropy | best | 0.521739 | 0.497203 |
| 3 | 0.100000 | 1 | entropy | random | 0.434783 | 0.461778 |
| 4 | 0.100000 | 1 | log_loss | best | 0.521739 | 0.497203 |
| 5 | 0.100000 | 1 | log_loss | random | 0.521739 | 0.494096 |
| 6 | 0.100000 | 2 | gini | best | 0.776398 | 0.766314 |
| 7 | 0.100000 | 2 | gini | random | 0.509317 | 0.552517 |
| 8 | 0.100000 | 2 | entropy | best | 0.770186 | 0.766314 |
| 9 | 0.100000 | 2 | entropy | random | 0.565217 | 0.597887 |
| 10 | 0.100000 | 2 | log_loss | best | 0.770186 | 0.766314 |
| 11 | 0.100000 | 2 | log_loss | random | 0.472050 | 0.455562 |
| 12 | 0.100000 | 3 | gini | best | 0.788820 | 0.775637 |
| 13 | 0.100000 | 3 | gini | random | 0.559006 | 0.582349 |
| 14 | 0.100000 | 3 | entropy | best | 0.770186 | 0.766314 |
| 15 | 0.100000 | 3 | entropy | random | 0.745342 | 0.729646 |
| 16 | 0.100000 | 3 | log_loss | best | 0.770186 | 0.766314 |
| 17 | 0.100000 | 3 | log_loss | random | 0.782609 | 0.719702 |
| 18 | 0.100000 | 4 | gini | best | 0.838509 | 0.839030 |
| 19 | 0.100000 | 4 | gini | random | 0.782609 | 0.770044 |
| 20 | 0.100000 | 4 | entropy | best | 0.826087 | 0.817278 |
| 21 | 0.100000 | 4 | entropy | random | 0.757764 | 0.769422 |
| 22 | 0.100000 | 4 | log_loss | best | 0.826087 | 0.817278 |
| 23 | 0.100000 | 4 | log_loss | random | 0.757764 | 0.747048 |
| 24 | 0.100000 | 5 | gini | best | 0.813665 | 0.881914 |
| 25 | 0.100000 | 5 | gini | random | 0.763975 | 0.768800 |
| 26 | 0.100000 | 5 | entropy | best | 0.788820 | 0.874456 |
| 27 | 0.100000 | 5 | entropy | random | 0.782609 | 0.789932 |
| 28 | 0.100000 | 5 | log_loss | best | 0.788820 | 0.874456 |
| 29 | 0.100000 | 5 | log_loss | random | 0.770186 | 0.773151 |
| 30 | 0.100000 | 6 | gini | best | 0.826087 | 0.916097 |
| 31 | 0.100000 | 6 | gini | random | 0.627329 | 0.691112 |
| 32 | 0.100000 | 6 | entropy | best | 0.801242 | 0.907396 |
| 33 | 0.100000 | 6 | entropy | random | 0.813665 | 0.806091 |
| 34 | 0.100000 | 6 | log_loss | best | 0.801242 | 0.907396 |
| 35 | 0.100000 | 6 | log_loss | random | 0.801242 | 0.814792 |
| 36 | 0.100000 | 7 | gini | best | 0.819876 | 0.941579 |
| 37 | 0.100000 | 7 | gini | random | 0.813665 | 0.870106 |
| 38 | 0.100000 | 7 | entropy | best | 0.826087 | 0.945308 |
| 39 | 0.100000 | 7 | entropy | random | 0.726708 | 0.745183 |
| 40 | 0.100000 | 7 | log_loss | best | 0.832298 | 0.945929 |
| 41 | 0.100000 | 7 | log_loss | random | 0.788820 | 0.855811 |
| 42 | 0.100000 | 8 | gini | best | 0.832298 | 0.962710 |
| 43 | 0.100000 | 8 | gini | random | 0.807453 | 0.856433 |
| 44 | 0.100000 | 8 | entropy | best | 0.857143 | 0.972032 |
| 45 | 0.100000 | 8 | entropy | random | 0.782609 | 0.835923 |
| 46 | 0.100000 | 8 | log_loss | best | 0.813665 | 0.967682 |
| 47 | 0.100000 | 8 | log_loss | random | 0.795031 | 0.853325 |
| 48 | 0.100000 | 9 | gini | best | 0.844720 | 0.972032 |
| 49 | 0.100000 | 9 | gini | random | 0.801242 | 0.896830 |
| 50 | 0.100000 | 9 | entropy | best | 0.838509 | 0.978247 |
| 51 | 0.100000 | 9 | entropy | random | 0.813665 | 0.924798 |
| 52 | 0.100000 | 9 | log_loss | best | 0.850932 | 0.979490 |
| 53 | 0.100000 | 9 | log_loss | random | 0.795031 | 0.914232 |
| 54 | 0.100000 | 10 | gini | best | 0.838509 | 0.979490 |
| 55 | 0.100000 | 10 | gini | random | 0.819876 | 0.957116 |
| 56 | 0.100000 | 10 | entropy | best | 0.863354 | 0.985084 |
| 57 | 0.100000 | 10 | entropy | random | 0.782609 | 0.935364 |
| 58 | 0.100000 | 10 | log_loss | best | 0.819876 | 0.980733 |
| 59 | 0.100000 | 10 | log_loss | random | 0.795031 | 0.942822 |
| 60 | 0.100000 | 11 | gini | best | 0.869565 | 0.985705 |
| 61 | 0.100000 | 11 | gini | random | 0.838509 | 0.960845 |
| 62 | 0.100000 | 11 | entropy | best | 0.850932 | 0.985084 |
| 63 | 0.100000 | 11 | entropy | random | 0.813665 | 0.934742 |
| 64 | 0.100000 | 11 | log_loss | best | 0.813665 | 0.981355 |
| 65 | 0.100000 | 11 | log_loss | random | 0.844720 | 0.937850 |
| 66 | 0.100000 | 12 | gini | best | 0.838509 | 0.983841 |
| 67 | 0.100000 | 12 | gini | random | 0.801242 | 0.976383 |
| 68 | 0.100000 | 12 | entropy | best | 0.844720 | 0.984462 |
| 69 | 0.100000 | 12 | entropy | random | 0.813665 | 0.948415 |
| 70 | 0.100000 | 12 | log_loss | best | 0.838509 | 0.983841 |
| 71 | 0.100000 | 12 | log_loss | random | 0.782609 | 0.971411 |
| 72 | 0.100000 | 13 | gini | best | 0.857143 | 0.985705 |
| 73 | 0.100000 | 13 | gini | random | 0.844720 | 0.981976 |
| 74 | 0.100000 | 13 | entropy | best | 0.844720 | 0.984462 |
| 75 | 0.100000 | 13 | entropy | random | 0.813665 | 0.970789 |
| 76 | 0.100000 | 13 | log_loss | best | 0.844720 | 0.984462 |
| 77 | 0.100000 | 13 | log_loss | random | 0.850932 | 0.967682 |
| 78 | 0.100000 | 14 | gini | best | 0.844720 | 0.984462 |
| 79 | 0.100000 | 14 | gini | random | 0.757764 | 0.971411 |
| 80 | 0.100000 | 14 | entropy | best | 0.826087 | 0.982598 |
| 81 | 0.100000 | 14 | entropy | random | 0.826087 | 0.981976 |
| 82 | 0.100000 | 14 | log_loss | best | 0.813665 | 0.981355 |
| 83 | 0.100000 | 14 | log_loss | random | 0.776398 | 0.977626 |
| 84 | 0.100000 | 15 | gini | best | 0.850932 | 0.985084 |
| 85 | 0.100000 | 15 | gini | random | 0.832298 | 0.983219 |
| 86 | 0.100000 | 15 | entropy | best | 0.813665 | 0.981355 |
| 87 | 0.100000 | 15 | entropy | random | 0.850932 | 0.983219 |
| 88 | 0.100000 | 15 | log_loss | best | 0.826087 | 0.982598 |
| 89 | 0.100000 | 15 | log_loss | random | 0.788820 | 0.977004 |
| 90 | 0.100000 | 16 | gini | best | 0.850932 | 0.985084 |
| 91 | 0.100000 | 16 | gini | random | 0.813665 | 0.981355 |
| 92 | 0.100000 | 16 | entropy | best | 0.826087 | 0.982598 |
| 93 | 0.100000 | 16 | entropy | random | 0.850932 | 0.984462 |
| 94 | 0.100000 | 16 | log_loss | best | 0.838509 | 0.983841 |
| 95 | 0.100000 | 16 | log_loss | random | 0.826087 | 0.982598 |
| 96 | 0.100000 | 17 | gini | best | 0.832298 | 0.983219 |
| 97 | 0.100000 | 17 | gini | random | 0.807453 | 0.980733 |
| 98 | 0.100000 | 17 | entropy | best | 0.832298 | 0.983219 |
| 99 | 0.100000 | 17 | entropy | random | 0.850932 | 0.984462 |
| 100 | 0.100000 | 17 | log_loss | best | 0.832298 | 0.983219 |
| 101 | 0.100000 | 17 | log_loss | random | 0.813665 | 0.981355 |
| 102 | 0.100000 | 18 | gini | best | 0.826087 | 0.982598 |
| 103 | 0.100000 | 18 | gini | random | 0.795031 | 0.979490 |
| 104 | 0.100000 | 18 | entropy | best | 0.832298 | 0.983219 |
| 105 | 0.100000 | 18 | entropy | random | 0.788820 | 0.978869 |
| 106 | 0.100000 | 18 | log_loss | best | 0.819876 | 0.981976 |
| 107 | 0.100000 | 18 | log_loss | random | 0.807453 | 0.980733 |
| 108 | 0.100000 | 19 | gini | best | 0.857143 | 0.985705 |
| 109 | 0.100000 | 19 | gini | random | 0.826087 | 0.982598 |
| 110 | 0.100000 | 19 | entropy | best | 0.844720 | 0.984462 |
| 111 | 0.100000 | 19 | entropy | random | 0.844720 | 0.984462 |
| 112 | 0.100000 | 19 | log_loss | best | 0.819876 | 0.981976 |
| 113 | 0.100000 | 19 | log_loss | random | 0.863354 | 0.986327 |
| 114 | 0.100000 | 20 | gini | best | 0.863354 | 0.986327 |
| 115 | 0.100000 | 20 | gini | random | 0.788820 | 0.978869 |
| 116 | 0.100000 | 20 | entropy | best | 0.826087 | 0.982598 |
| 117 | 0.100000 | 20 | entropy | random | 0.813665 | 0.981355 |
| 118 | 0.100000 | 20 | log_loss | best | 0.838509 | 0.983841 |
| 119 | 0.100000 | 20 | log_loss | random | 0.826087 | 0.982598 |
| 120 | 0.150000 | 1 | gini | best | 0.508264 | 0.497203 |
| 121 | 0.150000 | 1 | gini | random | 0.330579 | 0.341206 |
| 122 | 0.150000 | 1 | entropy | best | 0.508264 | 0.497203 |
| 123 | 0.150000 | 1 | entropy | random | 0.322314 | 0.329397 |
| 124 | 0.150000 | 1 | log_loss | best | 0.508264 | 0.497203 |
| 125 | 0.150000 | 1 | log_loss | random | 0.508264 | 0.496582 |
| 126 | 0.150000 | 2 | gini | best | 0.756198 | 0.764450 |
| 127 | 0.150000 | 2 | gini | random | 0.384298 | 0.338720 |
| 128 | 0.150000 | 2 | entropy | best | 0.760331 | 0.764450 |
| 129 | 0.150000 | 2 | entropy | random | 0.665289 | 0.625233 |
| 130 | 0.150000 | 2 | log_loss | best | 0.760331 | 0.764450 |
| 131 | 0.150000 | 2 | log_loss | random | 0.681818 | 0.724674 |
| 132 | 0.150000 | 3 | gini | best | 0.768595 | 0.773773 |
| 133 | 0.150000 | 3 | gini | random | 0.586777 | 0.569919 |
| 134 | 0.150000 | 3 | entropy | best | 0.768595 | 0.767557 |
| 135 | 0.150000 | 3 | entropy | random | 0.661157 | 0.661902 |
| 136 | 0.150000 | 3 | log_loss | best | 0.768595 | 0.767557 |
| 137 | 0.150000 | 3 | log_loss | random | 0.706612 | 0.709758 |
| 138 | 0.150000 | 4 | gini | best | 0.851240 | 0.839652 |
| 139 | 0.150000 | 4 | gini | random | 0.826446 | 0.794282 |
| 140 | 0.150000 | 4 | entropy | best | 0.851240 | 0.829708 |
| 141 | 0.150000 | 4 | entropy | random | 0.673554 | 0.691734 |
| 142 | 0.150000 | 4 | log_loss | best | 0.851240 | 0.829708 |
| 143 | 0.150000 | 4 | log_loss | random | 0.702479 | 0.704164 |
| 144 | 0.150000 | 5 | gini | best | 0.797521 | 0.881293 |
| 145 | 0.150000 | 5 | gini | random | 0.789256 | 0.822250 |
| 146 | 0.150000 | 5 | entropy | best | 0.838843 | 0.875699 |
| 147 | 0.150000 | 5 | entropy | random | 0.760331 | 0.742076 |
| 148 | 0.150000 | 5 | log_loss | best | 0.838843 | 0.875699 |
| 149 | 0.150000 | 5 | log_loss | random | 0.793388 | 0.761342 |
| 150 | 0.150000 | 6 | gini | best | 0.834711 | 0.915475 |
| 151 | 0.150000 | 6 | gini | random | 0.809917 | 0.806712 |
| 152 | 0.150000 | 6 | entropy | best | 0.851240 | 0.911125 |
| 153 | 0.150000 | 6 | entropy | random | 0.859504 | 0.835301 |
| 154 | 0.150000 | 6 | log_loss | best | 0.851240 | 0.911125 |
| 155 | 0.150000 | 6 | log_loss | random | 0.719008 | 0.750155 |
| 156 | 0.150000 | 7 | gini | best | 0.834711 | 0.937228 |
| 157 | 0.150000 | 7 | gini | random | 0.847107 | 0.879428 |
| 158 | 0.150000 | 7 | entropy | best | 0.855372 | 0.945308 |
| 159 | 0.150000 | 7 | entropy | random | 0.801653 | 0.868241 |
| 160 | 0.150000 | 7 | log_loss | best | 0.855372 | 0.945308 |
| 161 | 0.150000 | 7 | log_loss | random | 0.735537 | 0.770044 |
| 162 | 0.150000 | 8 | gini | best | 0.818182 | 0.954630 |
| 163 | 0.150000 | 8 | gini | random | 0.822314 | 0.870727 |
| 164 | 0.150000 | 8 | entropy | best | 0.847107 | 0.967060 |
| 165 | 0.150000 | 8 | entropy | random | 0.780992 | 0.848353 |
| 166 | 0.150000 | 8 | log_loss | best | 0.834711 | 0.965196 |
| 167 | 0.150000 | 8 | log_loss | random | 0.871901 | 0.912989 |
| 168 | 0.150000 | 9 | gini | best | 0.789256 | 0.960224 |
| 169 | 0.150000 | 9 | gini | random | 0.830579 | 0.882536 |
| 170 | 0.150000 | 9 | entropy | best | 0.859504 | 0.977004 |
| 171 | 0.150000 | 9 | entropy | random | 0.838843 | 0.922933 |
| 172 | 0.150000 | 9 | log_loss | best | 0.867769 | 0.978247 |
| 173 | 0.150000 | 9 | log_loss | random | 0.838843 | 0.885022 |
| 174 | 0.150000 | 10 | gini | best | 0.842975 | 0.971411 |
| 175 | 0.150000 | 10 | gini | random | 0.809917 | 0.939714 |
| 176 | 0.150000 | 10 | entropy | best | 0.871901 | 0.980733 |
| 177 | 0.150000 | 10 | entropy | random | 0.822314 | 0.934121 |
| 178 | 0.150000 | 10 | log_loss | best | 0.859504 | 0.978869 |
| 179 | 0.150000 | 10 | log_loss | random | 0.822314 | 0.938471 |
| 180 | 0.150000 | 11 | gini | best | 0.826446 | 0.972654 |
| 181 | 0.150000 | 11 | gini | random | 0.797521 | 0.867620 |
| 182 | 0.150000 | 11 | entropy | best | 0.842975 | 0.976383 |
| 183 | 0.150000 | 11 | entropy | random | 0.855372 | 0.967060 |
| 184 | 0.150000 | 11 | log_loss | best | 0.867769 | 0.980112 |
| 185 | 0.150000 | 11 | log_loss | random | 0.818182 | 0.948415 |
| 186 | 0.150000 | 12 | gini | best | 0.818182 | 0.972654 |
| 187 | 0.150000 | 12 | gini | random | 0.809917 | 0.963953 |
| 188 | 0.150000 | 12 | entropy | best | 0.851240 | 0.977626 |
| 189 | 0.150000 | 12 | entropy | random | 0.809917 | 0.968303 |
| 190 | 0.150000 | 12 | log_loss | best | 0.842975 | 0.976383 |
| 191 | 0.150000 | 12 | log_loss | random | 0.780992 | 0.959602 |
| 192 | 0.150000 | 13 | gini | best | 0.851240 | 0.977626 |
| 193 | 0.150000 | 13 | gini | random | 0.818182 | 0.971411 |
| 194 | 0.150000 | 13 | entropy | best | 0.863636 | 0.979490 |
| 195 | 0.150000 | 13 | entropy | random | 0.797521 | 0.949037 |
| 196 | 0.150000 | 13 | log_loss | best | 0.838843 | 0.975761 |
| 197 | 0.150000 | 13 | log_loss | random | 0.851240 | 0.975140 |
| 198 | 0.150000 | 14 | gini | best | 0.842975 | 0.976383 |
| 199 | 0.150000 | 14 | gini | random | 0.826446 | 0.969546 |
| 200 | 0.150000 | 14 | entropy | best | 0.855372 | 0.978247 |
| 201 | 0.150000 | 14 | entropy | random | 0.805785 | 0.970789 |
| 202 | 0.150000 | 14 | log_loss | best | 0.859504 | 0.978869 |
| 203 | 0.150000 | 14 | log_loss | random | 0.834711 | 0.968303 |
| 204 | 0.150000 | 15 | gini | best | 0.847107 | 0.977004 |
| 205 | 0.150000 | 15 | gini | random | 0.797521 | 0.969546 |
| 206 | 0.150000 | 15 | entropy | best | 0.847107 | 0.977004 |
| 207 | 0.150000 | 15 | entropy | random | 0.855372 | 0.978247 |
| 208 | 0.150000 | 15 | log_loss | best | 0.871901 | 0.980733 |
| 209 | 0.150000 | 15 | log_loss | random | 0.834711 | 0.975140 |
| 210 | 0.150000 | 16 | gini | best | 0.838843 | 0.975761 |
| 211 | 0.150000 | 16 | gini | random | 0.826446 | 0.973897 |
| 212 | 0.150000 | 16 | entropy | best | 0.847107 | 0.977004 |
| 213 | 0.150000 | 16 | entropy | random | 0.805785 | 0.970168 |
| 214 | 0.150000 | 16 | log_loss | best | 0.847107 | 0.977004 |
| 215 | 0.150000 | 16 | log_loss | random | 0.793388 | 0.968925 |
| 216 | 0.150000 | 17 | gini | best | 0.822314 | 0.973275 |
| 217 | 0.150000 | 17 | gini | random | 0.793388 | 0.968925 |
| 218 | 0.150000 | 17 | entropy | best | 0.851240 | 0.977626 |
| 219 | 0.150000 | 17 | entropy | random | 0.797521 | 0.969546 |
| 220 | 0.150000 | 17 | log_loss | best | 0.863636 | 0.979490 |
| 221 | 0.150000 | 17 | log_loss | random | 0.822314 | 0.973275 |
| 222 | 0.150000 | 18 | gini | best | 0.797521 | 0.969546 |
| 223 | 0.150000 | 18 | gini | random | 0.809917 | 0.971411 |
| 224 | 0.150000 | 18 | entropy | best | 0.863636 | 0.979490 |
| 225 | 0.150000 | 18 | entropy | random | 0.830579 | 0.973897 |
| 226 | 0.150000 | 18 | log_loss | best | 0.847107 | 0.977004 |
| 227 | 0.150000 | 18 | log_loss | random | 0.797521 | 0.969546 |
| 228 | 0.150000 | 19 | gini | best | 0.814050 | 0.972032 |
| 229 | 0.150000 | 19 | gini | random | 0.834711 | 0.975140 |
| 230 | 0.150000 | 19 | entropy | best | 0.855372 | 0.978247 |
| 231 | 0.150000 | 19 | entropy | random | 0.805785 | 0.970789 |
| 232 | 0.150000 | 19 | log_loss | best | 0.851240 | 0.977626 |
| 233 | 0.150000 | 19 | log_loss | random | 0.822314 | 0.973275 |
| 234 | 0.150000 | 20 | gini | best | 0.818182 | 0.972654 |
| 235 | 0.150000 | 20 | gini | random | 0.789256 | 0.968303 |
| 236 | 0.150000 | 20 | entropy | best | 0.855372 | 0.978247 |
| 237 | 0.150000 | 20 | entropy | random | 0.789256 | 0.968303 |
| 238 | 0.150000 | 20 | log_loss | best | 0.863636 | 0.979490 |
| 239 | 0.150000 | 20 | log_loss | random | 0.772727 | 0.965817 |
| 240 | 0.200000 | 1 | gini | best | 0.509317 | 0.497203 |
| 241 | 0.200000 | 1 | gini | random | 0.512422 | 0.484773 |
| 242 | 0.200000 | 1 | entropy | best | 0.509317 | 0.497203 |
| 243 | 0.200000 | 1 | entropy | random | 0.288820 | 0.334369 |
| 244 | 0.200000 | 1 | log_loss | best | 0.509317 | 0.497203 |
| 245 | 0.200000 | 1 | log_loss | random | 0.500000 | 0.491610 |
| 246 | 0.200000 | 2 | gini | best | 0.717391 | 0.757613 |
| 247 | 0.200000 | 2 | gini | random | 0.527950 | 0.567433 |
| 248 | 0.200000 | 2 | entropy | best | 0.726708 | 0.760721 |
| 249 | 0.200000 | 2 | entropy | random | 0.354037 | 0.402735 |
| 250 | 0.200000 | 2 | log_loss | best | 0.726708 | 0.760721 |
| 251 | 0.200000 | 2 | log_loss | random | 0.546584 | 0.597887 |
| 252 | 0.200000 | 3 | gini | best | 0.726708 | 0.766936 |
| 253 | 0.200000 | 3 | gini | random | 0.720497 | 0.756370 |
| 254 | 0.200000 | 3 | entropy | best | 0.732919 | 0.765693 |
| 255 | 0.200000 | 3 | entropy | random | 0.689441 | 0.719702 |
| 256 | 0.200000 | 3 | log_loss | best | 0.732919 | 0.765693 |
| 257 | 0.200000 | 3 | log_loss | random | 0.680124 | 0.707893 |
| 258 | 0.200000 | 4 | gini | best | 0.819876 | 0.827222 |
| 259 | 0.200000 | 4 | gini | random | 0.686335 | 0.720323 |
| 260 | 0.200000 | 4 | entropy | best | 0.801242 | 0.814170 |
| 261 | 0.200000 | 4 | entropy | random | 0.742236 | 0.762585 |
| 262 | 0.200000 | 4 | log_loss | best | 0.804348 | 0.814792 |
| 263 | 0.200000 | 4 | log_loss | random | 0.599379 | 0.626476 |
| 264 | 0.200000 | 5 | gini | best | 0.819876 | 0.877564 |
| 265 | 0.200000 | 5 | gini | random | 0.611801 | 0.655065 |
| 266 | 0.200000 | 5 | entropy | best | 0.822981 | 0.873835 |
| 267 | 0.200000 | 5 | entropy | random | 0.751553 | 0.760099 |
| 268 | 0.200000 | 5 | log_loss | best | 0.816770 | 0.872592 |
| 269 | 0.200000 | 5 | log_loss | random | 0.732919 | 0.778745 |
| 270 | 0.200000 | 6 | gini | best | 0.816770 | 0.908017 |
| 271 | 0.200000 | 6 | gini | random | 0.822981 | 0.857054 |
| 272 | 0.200000 | 6 | entropy | best | 0.826087 | 0.904910 |
| 273 | 0.200000 | 6 | entropy | random | 0.832298 | 0.839652 |
| 274 | 0.200000 | 6 | log_loss | best | 0.826087 | 0.904910 |
| 275 | 0.200000 | 6 | log_loss | random | 0.751553 | 0.750155 |
| 276 | 0.200000 | 7 | gini | best | 0.826087 | 0.935364 |
| 277 | 0.200000 | 7 | gini | random | 0.723602 | 0.789932 |
| 278 | 0.200000 | 7 | entropy | best | 0.829193 | 0.936607 |
| 279 | 0.200000 | 7 | entropy | random | 0.813665 | 0.847732 |
| 280 | 0.200000 | 7 | log_loss | best | 0.835404 | 0.937850 |
| 281 | 0.200000 | 7 | log_loss | random | 0.807453 | 0.857054 |
| 282 | 0.200000 | 8 | gini | best | 0.835404 | 0.956495 |
| 283 | 0.200000 | 8 | gini | random | 0.813665 | 0.905531 |
| 284 | 0.200000 | 8 | entropy | best | 0.832298 | 0.955873 |
| 285 | 0.200000 | 8 | entropy | random | 0.829193 | 0.901181 |
| 286 | 0.200000 | 8 | log_loss | best | 0.816770 | 0.953387 |
| 287 | 0.200000 | 8 | log_loss | random | 0.773292 | 0.840895 |
| 288 | 0.200000 | 9 | gini | best | 0.826087 | 0.960845 |
| 289 | 0.200000 | 9 | gini | random | 0.770186 | 0.892480 |
| 290 | 0.200000 | 9 | entropy | best | 0.829193 | 0.963953 |
| 291 | 0.200000 | 9 | entropy | random | 0.841615 | 0.913611 |
| 292 | 0.200000 | 9 | log_loss | best | 0.826087 | 0.963331 |
| 293 | 0.200000 | 9 | log_loss | random | 0.838509 | 0.929770 |
| 294 | 0.200000 | 10 | gini | best | 0.854037 | 0.970789 |
| 295 | 0.200000 | 10 | gini | random | 0.779503 | 0.881293 |
| 296 | 0.200000 | 10 | entropy | best | 0.813665 | 0.962710 |
| 297 | 0.200000 | 10 | entropy | random | 0.813665 | 0.937228 |
| 298 | 0.200000 | 10 | log_loss | best | 0.835404 | 0.967060 |
| 299 | 0.200000 | 10 | log_loss | random | 0.847826 | 0.937850 |
| 300 | 0.200000 | 11 | gini | best | 0.826087 | 0.965196 |
| 301 | 0.200000 | 11 | gini | random | 0.798137 | 0.953387 |
| 302 | 0.200000 | 11 | entropy | best | 0.826087 | 0.965196 |
| 303 | 0.200000 | 11 | entropy | random | 0.819876 | 0.960845 |
| 304 | 0.200000 | 11 | log_loss | best | 0.832298 | 0.966439 |
| 305 | 0.200000 | 11 | log_loss | random | 0.807453 | 0.938471 |
| 306 | 0.200000 | 12 | gini | best | 0.829193 | 0.965817 |
| 307 | 0.200000 | 12 | gini | random | 0.819876 | 0.962088 |
| 308 | 0.200000 | 12 | entropy | best | 0.826087 | 0.965196 |
| 309 | 0.200000 | 12 | entropy | random | 0.788820 | 0.945308 |
| 310 | 0.200000 | 12 | log_loss | best | 0.822981 | 0.964574 |
| 311 | 0.200000 | 12 | log_loss | random | 0.813665 | 0.950901 |
| 312 | 0.200000 | 13 | gini | best | 0.810559 | 0.962088 |
| 313 | 0.200000 | 13 | gini | random | 0.804348 | 0.959602 |
| 314 | 0.200000 | 13 | entropy | best | 0.838509 | 0.967682 |
| 315 | 0.200000 | 13 | entropy | random | 0.804348 | 0.950901 |
| 316 | 0.200000 | 13 | log_loss | best | 0.813665 | 0.962710 |
| 317 | 0.200000 | 13 | log_loss | random | 0.832298 | 0.958981 |
| 318 | 0.200000 | 14 | gini | best | 0.816770 | 0.963331 |
| 319 | 0.200000 | 14 | gini | random | 0.801242 | 0.960224 |
| 320 | 0.200000 | 14 | entropy | best | 0.819876 | 0.963953 |
| 321 | 0.200000 | 14 | entropy | random | 0.835404 | 0.966439 |
| 322 | 0.200000 | 14 | log_loss | best | 0.835404 | 0.967060 |
| 323 | 0.200000 | 14 | log_loss | random | 0.791925 | 0.957116 |
| 324 | 0.200000 | 15 | gini | best | 0.813665 | 0.962710 |
| 325 | 0.200000 | 15 | gini | random | 0.798137 | 0.956495 |
| 326 | 0.200000 | 15 | entropy | best | 0.829193 | 0.965817 |
| 327 | 0.200000 | 15 | entropy | random | 0.819876 | 0.963953 |
| 328 | 0.200000 | 15 | log_loss | best | 0.844720 | 0.968925 |
| 329 | 0.200000 | 15 | log_loss | random | 0.795031 | 0.956495 |
| 330 | 0.200000 | 16 | gini | best | 0.816770 | 0.963331 |
| 331 | 0.200000 | 16 | gini | random | 0.795031 | 0.952144 |
| 332 | 0.200000 | 16 | entropy | best | 0.847826 | 0.969546 |
| 333 | 0.200000 | 16 | entropy | random | 0.763975 | 0.947794 |
| 334 | 0.200000 | 16 | log_loss | best | 0.819876 | 0.963953 |
| 335 | 0.200000 | 16 | log_loss | random | 0.826087 | 0.965196 |
| 336 | 0.200000 | 17 | gini | best | 0.826087 | 0.965196 |
| 337 | 0.200000 | 17 | gini | random | 0.798137 | 0.959602 |
| 338 | 0.200000 | 17 | entropy | best | 0.829193 | 0.965817 |
| 339 | 0.200000 | 17 | entropy | random | 0.773292 | 0.954630 |
| 340 | 0.200000 | 17 | log_loss | best | 0.829193 | 0.965817 |
| 341 | 0.200000 | 17 | log_loss | random | 0.757764 | 0.951523 |
| 342 | 0.200000 | 18 | gini | best | 0.819876 | 0.963953 |
| 343 | 0.200000 | 18 | gini | random | 0.813665 | 0.962710 |
| 344 | 0.200000 | 18 | entropy | best | 0.822981 | 0.964574 |
| 345 | 0.200000 | 18 | entropy | random | 0.841615 | 0.968303 |
| 346 | 0.200000 | 18 | log_loss | best | 0.850932 | 0.970168 |
| 347 | 0.200000 | 18 | log_loss | random | 0.866460 | 0.973275 |
| 348 | 0.200000 | 19 | gini | best | 0.822981 | 0.964574 |
| 349 | 0.200000 | 19 | gini | random | 0.801242 | 0.960224 |
| 350 | 0.200000 | 19 | entropy | best | 0.829193 | 0.965817 |
| 351 | 0.200000 | 19 | entropy | random | 0.822981 | 0.964574 |
| 352 | 0.200000 | 19 | log_loss | best | 0.826087 | 0.965196 |
| 353 | 0.200000 | 19 | log_loss | random | 0.773292 | 0.954630 |
| 354 | 0.200000 | 20 | gini | best | 0.822981 | 0.964574 |
| 355 | 0.200000 | 20 | gini | random | 0.810559 | 0.962088 |
| 356 | 0.200000 | 20 | entropy | best | 0.841615 | 0.968303 |
| 357 | 0.200000 | 20 | entropy | random | 0.791925 | 0.958359 |
| 358 | 0.200000 | 20 | log_loss | best | 0.850932 | 0.970168 |
| 359 | 0.200000 | 20 | log_loss | random | 0.801242 | 0.960224 |
| 360 | 0.250000 | 1 | gini | best | 0.501241 | 0.497203 |
| 361 | 0.250000 | 1 | gini | random | 0.372208 | 0.413922 |
| 362 | 0.250000 | 1 | entropy | best | 0.501241 | 0.497203 |
| 363 | 0.250000 | 1 | entropy | random | 0.501241 | 0.496582 |
| 364 | 0.250000 | 1 | log_loss | best | 0.501241 | 0.497203 |
| 365 | 0.250000 | 1 | log_loss | random | 0.384615 | 0.425730 |
| 366 | 0.250000 | 2 | gini | best | 0.719603 | 0.760099 |
| 367 | 0.250000 | 2 | gini | random | 0.498759 | 0.495960 |
| 368 | 0.250000 | 2 | entropy | best | 0.719603 | 0.760099 |
| 369 | 0.250000 | 2 | entropy | random | 0.441687 | 0.477315 |
| 370 | 0.250000 | 2 | log_loss | best | 0.719603 | 0.760099 |
| 371 | 0.250000 | 2 | log_loss | random | 0.605459 | 0.660037 |
| 372 | 0.250000 | 3 | gini | best | 0.734491 | 0.770044 |
| 373 | 0.250000 | 3 | gini | random | 0.617866 | 0.684897 |
| 374 | 0.250000 | 3 | entropy | best | 0.729529 | 0.765071 |
| 375 | 0.250000 | 3 | entropy | random | 0.662531 | 0.639528 |
| 376 | 0.250000 | 3 | log_loss | best | 0.729529 | 0.765071 |
| 377 | 0.250000 | 3 | log_loss | random | 0.667494 | 0.669981 |
| 378 | 0.250000 | 4 | gini | best | 0.801489 | 0.822871 |
| 379 | 0.250000 | 4 | gini | random | 0.684864 | 0.730267 |
| 380 | 0.250000 | 4 | entropy | best | 0.794045 | 0.812306 |
| 381 | 0.250000 | 4 | entropy | random | 0.714640 | 0.752020 |
| 382 | 0.250000 | 4 | log_loss | best | 0.781638 | 0.809198 |
| 383 | 0.250000 | 4 | log_loss | random | 0.756824 | 0.775016 |
| 384 | 0.250000 | 5 | gini | best | 0.828784 | 0.881293 |
| 385 | 0.250000 | 5 | gini | random | 0.692308 | 0.711622 |
| 386 | 0.250000 | 5 | entropy | best | 0.828784 | 0.870727 |
| 387 | 0.250000 | 5 | entropy | random | 0.707196 | 0.755749 |
| 388 | 0.250000 | 5 | log_loss | best | 0.828784 | 0.870727 |
| 389 | 0.250000 | 5 | log_loss | random | 0.669975 | 0.728403 |
| 390 | 0.250000 | 6 | gini | best | 0.806452 | 0.902424 |
| 391 | 0.250000 | 6 | gini | random | 0.796526 | 0.824114 |
| 392 | 0.250000 | 6 | entropy | best | 0.838710 | 0.897452 |
| 393 | 0.250000 | 6 | entropy | random | 0.781638 | 0.785581 |
| 394 | 0.250000 | 6 | log_loss | best | 0.828784 | 0.894966 |
| 395 | 0.250000 | 6 | log_loss | random | 0.677419 | 0.689248 |
| 396 | 0.250000 | 7 | gini | best | 0.828784 | 0.931635 |
| 397 | 0.250000 | 7 | gini | random | 0.789082 | 0.863269 |
| 398 | 0.250000 | 7 | entropy | best | 0.863524 | 0.938471 |
| 399 | 0.250000 | 7 | entropy | random | 0.796526 | 0.866998 |
| 400 | 0.250000 | 7 | log_loss | best | 0.861042 | 0.937850 |
| 401 | 0.250000 | 7 | log_loss | random | 0.759305 | 0.812927 |
| 402 | 0.250000 | 8 | gini | best | 0.811414 | 0.941579 |
| 403 | 0.250000 | 8 | gini | random | 0.806452 | 0.870106 |
| 404 | 0.250000 | 8 | entropy | best | 0.846154 | 0.950901 |
| 405 | 0.250000 | 8 | entropy | random | 0.831266 | 0.861405 |
| 406 | 0.250000 | 8 | log_loss | best | 0.838710 | 0.949037 |
| 407 | 0.250000 | 8 | log_loss | random | 0.801489 | 0.895587 |
| 408 | 0.250000 | 9 | gini | best | 0.821340 | 0.949037 |
| 409 | 0.250000 | 9 | gini | random | 0.803970 | 0.895587 |
| 410 | 0.250000 | 9 | entropy | best | 0.848635 | 0.960845 |
| 411 | 0.250000 | 9 | entropy | random | 0.776675 | 0.883157 |
| 412 | 0.250000 | 9 | log_loss | best | 0.848635 | 0.960224 |
| 413 | 0.250000 | 9 | log_loss | random | 0.808933 | 0.908639 |
| 414 | 0.250000 | 10 | gini | best | 0.799007 | 0.947172 |
| 415 | 0.250000 | 10 | gini | random | 0.823821 | 0.944686 |
| 416 | 0.250000 | 10 | entropy | best | 0.868486 | 0.967060 |
| 417 | 0.250000 | 10 | entropy | random | 0.801489 | 0.894966 |
| 418 | 0.250000 | 10 | log_loss | best | 0.838710 | 0.959602 |
| 419 | 0.250000 | 10 | log_loss | random | 0.779156 | 0.903667 |
| 420 | 0.250000 | 11 | gini | best | 0.799007 | 0.949037 |
| 421 | 0.250000 | 11 | gini | random | 0.796526 | 0.927906 |
| 422 | 0.250000 | 11 | entropy | best | 0.848635 | 0.962088 |
| 423 | 0.250000 | 11 | entropy | random | 0.781638 | 0.919826 |
| 424 | 0.250000 | 11 | log_loss | best | 0.843672 | 0.960845 |
| 425 | 0.250000 | 11 | log_loss | random | 0.821340 | 0.929149 |
| 426 | 0.250000 | 12 | gini | best | 0.791563 | 0.947794 |
| 427 | 0.250000 | 12 | gini | random | 0.801489 | 0.947794 |
| 428 | 0.250000 | 12 | entropy | best | 0.848635 | 0.962088 |
| 429 | 0.250000 | 12 | entropy | random | 0.811414 | 0.945929 |
| 430 | 0.250000 | 12 | log_loss | best | 0.841191 | 0.960224 |
| 431 | 0.250000 | 12 | log_loss | random | 0.799007 | 0.914854 |
| 432 | 0.250000 | 13 | gini | best | 0.803970 | 0.950901 |
| 433 | 0.250000 | 13 | gini | random | 0.799007 | 0.949658 |
| 434 | 0.250000 | 13 | entropy | best | 0.848635 | 0.962088 |
| 435 | 0.250000 | 13 | entropy | random | 0.811414 | 0.940336 |
| 436 | 0.250000 | 13 | log_loss | best | 0.853598 | 0.963331 |
| 437 | 0.250000 | 13 | log_loss | random | 0.818859 | 0.948415 |
| 438 | 0.250000 | 14 | gini | best | 0.801489 | 0.950280 |
| 439 | 0.250000 | 14 | gini | random | 0.799007 | 0.949658 |
| 440 | 0.250000 | 14 | entropy | best | 0.848635 | 0.962088 |
| 441 | 0.250000 | 14 | entropy | random | 0.799007 | 0.949658 |
| 442 | 0.250000 | 14 | log_loss | best | 0.853598 | 0.963331 |
| 443 | 0.250000 | 14 | log_loss | random | 0.759305 | 0.939093 |
| 444 | 0.250000 | 15 | gini | best | 0.806452 | 0.951523 |
| 445 | 0.250000 | 15 | gini | random | 0.784119 | 0.945929 |
| 446 | 0.250000 | 15 | entropy | best | 0.853598 | 0.963331 |
| 447 | 0.250000 | 15 | entropy | random | 0.813896 | 0.953387 |
| 448 | 0.250000 | 15 | log_loss | best | 0.851117 | 0.962710 |
| 449 | 0.250000 | 15 | log_loss | random | 0.838710 | 0.958359 |
| 450 | 0.250000 | 16 | gini | best | 0.799007 | 0.949658 |
| 451 | 0.250000 | 16 | gini | random | 0.779156 | 0.944686 |
| 452 | 0.250000 | 16 | entropy | best | 0.846154 | 0.961467 |
| 453 | 0.250000 | 16 | entropy | random | 0.808933 | 0.952144 |
| 454 | 0.250000 | 16 | log_loss | best | 0.848635 | 0.962088 |
| 455 | 0.250000 | 16 | log_loss | random | 0.851117 | 0.962710 |
| 456 | 0.250000 | 17 | gini | best | 0.816377 | 0.954009 |
| 457 | 0.250000 | 17 | gini | random | 0.806452 | 0.951523 |
| 458 | 0.250000 | 17 | entropy | best | 0.858561 | 0.964574 |
| 459 | 0.250000 | 17 | entropy | random | 0.828784 | 0.957116 |
| 460 | 0.250000 | 17 | log_loss | best | 0.851117 | 0.962710 |
| 461 | 0.250000 | 17 | log_loss | random | 0.823821 | 0.955873 |
| 462 | 0.250000 | 18 | gini | best | 0.784119 | 0.945929 |
| 463 | 0.250000 | 18 | gini | random | 0.799007 | 0.949658 |
| 464 | 0.250000 | 18 | entropy | best | 0.843672 | 0.960845 |
| 465 | 0.250000 | 18 | entropy | random | 0.801489 | 0.950280 |
| 466 | 0.250000 | 18 | log_loss | best | 0.851117 | 0.962710 |
| 467 | 0.250000 | 18 | log_loss | random | 0.791563 | 0.947794 |
| 468 | 0.250000 | 19 | gini | best | 0.803970 | 0.950901 |
| 469 | 0.250000 | 19 | gini | random | 0.781638 | 0.945308 |
| 470 | 0.250000 | 19 | entropy | best | 0.851117 | 0.962710 |
| 471 | 0.250000 | 19 | entropy | random | 0.816377 | 0.954009 |
| 472 | 0.250000 | 19 | log_loss | best | 0.843672 | 0.960845 |
| 473 | 0.250000 | 19 | log_loss | random | 0.784119 | 0.945929 |
| 474 | 0.250000 | 20 | gini | best | 0.816377 | 0.954009 |
| 475 | 0.250000 | 20 | gini | random | 0.806452 | 0.951523 |
| 476 | 0.250000 | 20 | entropy | best | 0.846154 | 0.961467 |
| 477 | 0.250000 | 20 | entropy | random | 0.786600 | 0.946551 |
| 478 | 0.250000 | 20 | log_loss | best | 0.861042 | 0.965196 |
| 479 | 0.250000 | 20 | log_loss | random | 0.801489 | 0.950280 |
| 480 | 0.300000 | 1 | gini | best | 0.496894 | 0.497203 |
| 481 | 0.300000 | 1 | gini | random | 0.438923 | 0.459913 |
| 482 | 0.300000 | 1 | entropy | best | 0.496894 | 0.497203 |
| 483 | 0.300000 | 1 | entropy | random | 0.360248 | 0.397141 |
| 484 | 0.300000 | 1 | log_loss | best | 0.496894 | 0.497203 |
| 485 | 0.300000 | 1 | log_loss | random | 0.279503 | 0.305780 |
| 486 | 0.300000 | 2 | gini | best | 0.726708 | 0.756992 |
| 487 | 0.300000 | 2 | gini | random | 0.716356 | 0.731510 |
| 488 | 0.300000 | 2 | entropy | best | 0.730849 | 0.758235 |
| 489 | 0.300000 | 2 | entropy | random | 0.548654 | 0.551896 |
| 490 | 0.300000 | 2 | log_loss | best | 0.730849 | 0.758235 |
| 491 | 0.300000 | 2 | log_loss | random | 0.540373 | 0.523928 |
| 492 | 0.300000 | 3 | gini | best | 0.726708 | 0.761342 |
| 493 | 0.300000 | 3 | gini | random | 0.507246 | 0.539466 |
| 494 | 0.300000 | 3 | entropy | best | 0.730849 | 0.758235 |
| 495 | 0.300000 | 3 | entropy | random | 0.681159 | 0.703543 |
| 496 | 0.300000 | 3 | log_loss | best | 0.730849 | 0.758235 |
| 497 | 0.300000 | 3 | log_loss | random | 0.658385 | 0.676818 |
| 498 | 0.300000 | 4 | gini | best | 0.801242 | 0.822871 |
| 499 | 0.300000 | 4 | gini | random | 0.772257 | 0.768179 |
| 500 | 0.300000 | 4 | entropy | best | 0.803313 | 0.817278 |
| 501 | 0.300000 | 4 | entropy | random | 0.693582 | 0.696706 |
| 502 | 0.300000 | 4 | log_loss | best | 0.803313 | 0.817278 |
| 503 | 0.300000 | 4 | log_loss | random | 0.701863 | 0.732753 |
| 504 | 0.300000 | 5 | gini | best | 0.842650 | 0.881293 |
| 505 | 0.300000 | 5 | gini | random | 0.772257 | 0.783717 |
| 506 | 0.300000 | 5 | entropy | best | 0.826087 | 0.866998 |
| 507 | 0.300000 | 5 | entropy | random | 0.743271 | 0.766936 |
| 508 | 0.300000 | 5 | log_loss | best | 0.826087 | 0.866998 |
| 509 | 0.300000 | 5 | log_loss | random | 0.660455 | 0.668117 |
| 510 | 0.300000 | 6 | gini | best | 0.842650 | 0.908017 |
| 511 | 0.300000 | 6 | gini | random | 0.792961 | 0.815413 |
| 512 | 0.300000 | 6 | entropy | best | 0.828157 | 0.893723 |
| 513 | 0.300000 | 6 | entropy | random | 0.776398 | 0.836544 |
| 514 | 0.300000 | 6 | log_loss | best | 0.826087 | 0.893101 |
| 515 | 0.300000 | 6 | log_loss | random | 0.693582 | 0.741454 |
| 516 | 0.300000 | 7 | gini | best | 0.819876 | 0.921069 |
| 517 | 0.300000 | 7 | gini | random | 0.819876 | 0.875699 |
| 518 | 0.300000 | 7 | entropy | best | 0.848861 | 0.932256 |
| 519 | 0.300000 | 7 | entropy | random | 0.792961 | 0.855190 |
| 520 | 0.300000 | 7 | log_loss | best | 0.855072 | 0.934121 |
| 521 | 0.300000 | 7 | log_loss | random | 0.790890 | 0.821628 |
| 522 | 0.300000 | 8 | gini | best | 0.850932 | 0.947172 |
| 523 | 0.300000 | 8 | gini | random | 0.807453 | 0.852082 |
| 524 | 0.300000 | 8 | entropy | best | 0.817805 | 0.935364 |
| 525 | 0.300000 | 8 | entropy | random | 0.780538 | 0.825357 |
| 526 | 0.300000 | 8 | log_loss | best | 0.817805 | 0.935364 |
| 527 | 0.300000 | 8 | log_loss | random | 0.797101 | 0.829086 |
| 528 | 0.300000 | 9 | gini | best | 0.826087 | 0.945308 |
| 529 | 0.300000 | 9 | gini | random | 0.799172 | 0.875078 |
| 530 | 0.300000 | 9 | entropy | best | 0.826087 | 0.947172 |
| 531 | 0.300000 | 9 | entropy | random | 0.784679 | 0.893723 |
| 532 | 0.300000 | 9 | log_loss | best | 0.838509 | 0.951523 |
| 533 | 0.300000 | 9 | log_loss | random | 0.817805 | 0.906153 |
| 534 | 0.300000 | 10 | gini | best | 0.815735 | 0.944065 |
| 535 | 0.300000 | 10 | gini | random | 0.778468 | 0.890615 |
| 536 | 0.300000 | 10 | entropy | best | 0.830228 | 0.949037 |
| 537 | 0.300000 | 10 | entropy | random | 0.774327 | 0.880671 |
| 538 | 0.300000 | 10 | log_loss | best | 0.828157 | 0.948415 |
| 539 | 0.300000 | 10 | log_loss | random | 0.850932 | 0.919826 |
| 540 | 0.300000 | 11 | gini | best | 0.821946 | 0.946551 |
| 541 | 0.300000 | 11 | gini | random | 0.807453 | 0.930392 |
| 542 | 0.300000 | 11 | entropy | best | 0.836439 | 0.950901 |
| 543 | 0.300000 | 11 | entropy | random | 0.824017 | 0.930392 |
| 544 | 0.300000 | 11 | log_loss | best | 0.848861 | 0.954630 |
| 545 | 0.300000 | 11 | log_loss | random | 0.778468 | 0.906774 |
| 546 | 0.300000 | 12 | gini | best | 0.830228 | 0.949037 |
| 547 | 0.300000 | 12 | gini | random | 0.776398 | 0.932878 |
| 548 | 0.300000 | 12 | entropy | best | 0.840580 | 0.952144 |
| 549 | 0.300000 | 12 | entropy | random | 0.813665 | 0.937850 |
| 550 | 0.300000 | 12 | log_loss | best | 0.848861 | 0.954630 |
| 551 | 0.300000 | 12 | log_loss | random | 0.824017 | 0.939714 |
| 552 | 0.300000 | 13 | gini | best | 0.828157 | 0.948415 |
| 553 | 0.300000 | 13 | gini | random | 0.778468 | 0.933499 |
| 554 | 0.300000 | 13 | entropy | best | 0.828157 | 0.948415 |
| 555 | 0.300000 | 13 | entropy | random | 0.809524 | 0.938471 |
| 556 | 0.300000 | 13 | log_loss | best | 0.828157 | 0.948415 |
| 557 | 0.300000 | 13 | log_loss | random | 0.797101 | 0.936607 |
| 558 | 0.300000 | 14 | gini | best | 0.828157 | 0.948415 |
| 559 | 0.300000 | 14 | gini | random | 0.768116 | 0.929770 |
| 560 | 0.300000 | 14 | entropy | best | 0.834369 | 0.950280 |
| 561 | 0.300000 | 14 | entropy | random | 0.836439 | 0.950280 |
| 562 | 0.300000 | 14 | log_loss | best | 0.834369 | 0.950280 |
| 563 | 0.300000 | 14 | log_loss | random | 0.809524 | 0.942822 |
| 564 | 0.300000 | 15 | gini | best | 0.838509 | 0.951523 |
| 565 | 0.300000 | 15 | gini | random | 0.778468 | 0.920447 |
| 566 | 0.300000 | 15 | entropy | best | 0.844720 | 0.953387 |
| 567 | 0.300000 | 15 | entropy | random | 0.824017 | 0.942822 |
| 568 | 0.300000 | 15 | log_loss | best | 0.834369 | 0.950280 |
| 569 | 0.300000 | 15 | log_loss | random | 0.795031 | 0.938471 |
| 570 | 0.300000 | 16 | gini | best | 0.819876 | 0.945929 |
| 571 | 0.300000 | 16 | gini | random | 0.788820 | 0.936607 |
| 572 | 0.300000 | 16 | entropy | best | 0.826087 | 0.947794 |
| 573 | 0.300000 | 16 | entropy | random | 0.819876 | 0.945308 |
| 574 | 0.300000 | 16 | log_loss | best | 0.832298 | 0.949658 |
| 575 | 0.300000 | 16 | log_loss | random | 0.778468 | 0.933499 |
| 576 | 0.300000 | 17 | gini | best | 0.803313 | 0.940957 |
| 577 | 0.300000 | 17 | gini | random | 0.797101 | 0.939093 |
| 578 | 0.300000 | 17 | entropy | best | 0.830228 | 0.949037 |
| 579 | 0.300000 | 17 | entropy | random | 0.809524 | 0.942822 |
| 580 | 0.300000 | 17 | log_loss | best | 0.838509 | 0.951523 |
| 581 | 0.300000 | 17 | log_loss | random | 0.788820 | 0.936607 |
| 582 | 0.300000 | 18 | gini | best | 0.830228 | 0.949037 |
| 583 | 0.300000 | 18 | gini | random | 0.786749 | 0.935985 |
| 584 | 0.300000 | 18 | entropy | best | 0.834369 | 0.950280 |
| 585 | 0.300000 | 18 | entropy | random | 0.809524 | 0.942822 |
| 586 | 0.300000 | 18 | log_loss | best | 0.842650 | 0.952766 |
| 587 | 0.300000 | 18 | log_loss | random | 0.801242 | 0.940336 |
| 588 | 0.300000 | 19 | gini | best | 0.811594 | 0.943443 |
| 589 | 0.300000 | 19 | gini | random | 0.795031 | 0.938471 |
| 590 | 0.300000 | 19 | entropy | best | 0.836439 | 0.950901 |
| 591 | 0.300000 | 19 | entropy | random | 0.815735 | 0.944686 |
| 592 | 0.300000 | 19 | log_loss | best | 0.853002 | 0.955873 |
| 593 | 0.300000 | 19 | log_loss | random | 0.784679 | 0.935364 |
| 594 | 0.300000 | 20 | gini | best | 0.813665 | 0.944065 |
| 595 | 0.300000 | 20 | gini | random | 0.809524 | 0.942822 |
| 596 | 0.300000 | 20 | entropy | best | 0.832298 | 0.949658 |
| 597 | 0.300000 | 20 | entropy | random | 0.809524 | 0.942822 |
| 598 | 0.300000 | 20 | log_loss | best | 0.840580 | 0.952144 |
| 599 | 0.300000 | 20 | log_loss | random | 0.836439 | 0.950901 |
Row:60, testsize=0.1, Max_depth=11, Criterion=gini, Splitter=best, Accuracy=0.881988, Score=0.986948
In [44]:
X_train, X_test, y_train, y_test = train_test_split(Xd, yd, test_size=0.1, random_state=0)
In [45]:
clf_dts = DecisionTreeClassifier(max_depth=11, criterion='gini', splitter='best')
clf_dts.fit(X_train, y_train)
y_pred = clf_dts.predict(X_test)
In [46]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_dts.score(Xd, yd))
Accuracy = 0.8757763975155279 Score = 0.9863269111249223
RandomForest¶
In [47]:
Xr = df_train.drop('price_range', axis=1)
yr = df_train.price_range.values.reshape(-1, 1)
In [48]:
def RF(Xr, yr, Testsize, mdepth):
df_evaluation = pd.DataFrame()
for x in Testsize:
X_train, X_test, y_train, y_test = train_test_split(Xr, yr, test_size=x, random_state=0)
for maxdepth in mdepth:
for n_estimators in [10, 50, 100, 200, 300]:
for criterion in ["gini", "entropy", "log_loss"]:
RF = RandomForestClassifier(n_estimators=n_estimators,
criterion=criterion,
max_depth=maxdepth)
RF.fit(X_train, y_train)
y_pred = RF.predict(X_test)
dict = {'Test_size': x,
"Max_depth": maxdepth,
"n_estimators": n_estimators,
"criterion": criterion,
'acc': metrics.accuracy_score(y_test,y_pred),
"score": RF.score(Xr, yr)}
df_evaluation = pd.concat([df_evaluation, pd.DataFrame(dict, index=[0])], ignore_index=True)
return(df_evaluation)
df_evaluation = RF(Xr, yr, [.1,.15,.2,.25,.3], range(1,21))
df_evaluation.style.apply(highlight_max)
Out[48]:
| Test_size | Max_depth | n_estimators | criterion | acc | score | |
|---|---|---|---|---|---|---|
| 0 | 0.100000 | 1 | 10 | gini | 0.565217 | 0.533872 |
| 1 | 0.100000 | 1 | 10 | entropy | 0.527950 | 0.523306 |
| 2 | 0.100000 | 1 | 10 | log_loss | 0.527950 | 0.528278 |
| 3 | 0.100000 | 1 | 50 | gini | 0.621118 | 0.628341 |
| 4 | 0.100000 | 1 | 50 | entropy | 0.540373 | 0.563704 |
| 5 | 0.100000 | 1 | 50 | log_loss | 0.614907 | 0.576756 |
| 6 | 0.100000 | 1 | 100 | gini | 0.670807 | 0.679304 |
| 7 | 0.100000 | 1 | 100 | entropy | 0.596273 | 0.582971 |
| 8 | 0.100000 | 1 | 100 | log_loss | 0.590062 | 0.566812 |
| 9 | 0.100000 | 1 | 200 | gini | 0.677019 | 0.666252 |
| 10 | 0.100000 | 1 | 200 | entropy | 0.602484 | 0.587321 |
| 11 | 0.100000 | 1 | 200 | log_loss | 0.590062 | 0.589807 |
| 12 | 0.100000 | 1 | 300 | gini | 0.664596 | 0.696085 |
| 13 | 0.100000 | 1 | 300 | entropy | 0.608696 | 0.576756 |
| 14 | 0.100000 | 1 | 300 | log_loss | 0.621118 | 0.587321 |
| 15 | 0.100000 | 2 | 10 | gini | 0.658385 | 0.679925 |
| 16 | 0.100000 | 2 | 10 | entropy | 0.658385 | 0.663145 |
| 17 | 0.100000 | 2 | 10 | log_loss | 0.571429 | 0.575513 |
| 18 | 0.100000 | 2 | 50 | gini | 0.726708 | 0.791175 |
| 19 | 0.100000 | 2 | 50 | entropy | 0.801242 | 0.805469 |
| 20 | 0.100000 | 2 | 50 | log_loss | 0.720497 | 0.727781 |
| 21 | 0.100000 | 2 | 100 | gini | 0.739130 | 0.764450 |
| 22 | 0.100000 | 2 | 100 | entropy | 0.664596 | 0.690491 |
| 23 | 0.100000 | 2 | 100 | log_loss | 0.664596 | 0.717837 |
| 24 | 0.100000 | 2 | 200 | gini | 0.763975 | 0.795525 |
| 25 | 0.100000 | 2 | 200 | entropy | 0.726708 | 0.740211 |
| 26 | 0.100000 | 2 | 200 | log_loss | 0.708075 | 0.701057 |
| 27 | 0.100000 | 2 | 300 | gini | 0.782609 | 0.796147 |
| 28 | 0.100000 | 2 | 300 | entropy | 0.739130 | 0.712865 |
| 29 | 0.100000 | 2 | 300 | log_loss | 0.739130 | 0.753263 |
| 30 | 0.100000 | 3 | 10 | gini | 0.689441 | 0.755749 |
| 31 | 0.100000 | 3 | 10 | entropy | 0.596273 | 0.646364 |
| 32 | 0.100000 | 3 | 10 | log_loss | 0.695652 | 0.721566 |
| 33 | 0.100000 | 3 | 50 | gini | 0.813665 | 0.825979 |
| 34 | 0.100000 | 3 | 50 | entropy | 0.807453 | 0.812927 |
| 35 | 0.100000 | 3 | 50 | log_loss | 0.807453 | 0.829708 |
| 36 | 0.100000 | 3 | 100 | gini | 0.807453 | 0.837787 |
| 37 | 0.100000 | 3 | 100 | entropy | 0.813665 | 0.841516 |
| 38 | 0.100000 | 3 | 100 | log_loss | 0.745342 | 0.796147 |
| 39 | 0.100000 | 3 | 200 | gini | 0.788820 | 0.842138 |
| 40 | 0.100000 | 3 | 200 | entropy | 0.788820 | 0.837166 |
| 41 | 0.100000 | 3 | 200 | log_loss | 0.782609 | 0.819142 |
| 42 | 0.100000 | 3 | 300 | gini | 0.832298 | 0.852704 |
| 43 | 0.100000 | 3 | 300 | entropy | 0.782609 | 0.835301 |
| 44 | 0.100000 | 3 | 300 | log_loss | 0.819876 | 0.844624 |
| 45 | 0.100000 | 4 | 10 | gini | 0.708075 | 0.764450 |
| 46 | 0.100000 | 4 | 10 | entropy | 0.726708 | 0.798633 |
| 47 | 0.100000 | 4 | 10 | log_loss | 0.788820 | 0.831572 |
| 48 | 0.100000 | 4 | 50 | gini | 0.819876 | 0.882536 |
| 49 | 0.100000 | 4 | 50 | entropy | 0.782609 | 0.845245 |
| 50 | 0.100000 | 4 | 50 | log_loss | 0.819876 | 0.862648 |
| 51 | 0.100000 | 4 | 100 | gini | 0.813665 | 0.884400 |
| 52 | 0.100000 | 4 | 100 | entropy | 0.844720 | 0.867620 |
| 53 | 0.100000 | 4 | 100 | log_loss | 0.788820 | 0.867620 |
| 54 | 0.100000 | 4 | 200 | gini | 0.807453 | 0.889994 |
| 55 | 0.100000 | 4 | 200 | entropy | 0.838509 | 0.880050 |
| 56 | 0.100000 | 4 | 200 | log_loss | 0.795031 | 0.865134 |
| 57 | 0.100000 | 4 | 300 | gini | 0.819876 | 0.881914 |
| 58 | 0.100000 | 4 | 300 | entropy | 0.807453 | 0.870727 |
| 59 | 0.100000 | 4 | 300 | log_loss | 0.826087 | 0.871349 |
| 60 | 0.100000 | 5 | 10 | gini | 0.832298 | 0.885022 |
| 61 | 0.100000 | 5 | 10 | entropy | 0.757764 | 0.821007 |
| 62 | 0.100000 | 5 | 10 | log_loss | 0.782609 | 0.852704 |
| 63 | 0.100000 | 5 | 50 | gini | 0.869565 | 0.908639 |
| 64 | 0.100000 | 5 | 50 | entropy | 0.832298 | 0.901181 |
| 65 | 0.100000 | 5 | 50 | log_loss | 0.801242 | 0.887508 |
| 66 | 0.100000 | 5 | 100 | gini | 0.850932 | 0.917340 |
| 67 | 0.100000 | 5 | 100 | entropy | 0.813665 | 0.899938 |
| 68 | 0.100000 | 5 | 100 | log_loss | 0.832298 | 0.897452 |
| 69 | 0.100000 | 5 | 200 | gini | 0.838509 | 0.916718 |
| 70 | 0.100000 | 5 | 200 | entropy | 0.838509 | 0.908017 |
| 71 | 0.100000 | 5 | 200 | log_loss | 0.844720 | 0.908017 |
| 72 | 0.100000 | 5 | 300 | gini | 0.850932 | 0.925420 |
| 73 | 0.100000 | 5 | 300 | entropy | 0.838509 | 0.905531 |
| 74 | 0.100000 | 5 | 300 | log_loss | 0.832298 | 0.906774 |
| 75 | 0.100000 | 6 | 10 | gini | 0.807453 | 0.876321 |
| 76 | 0.100000 | 6 | 10 | entropy | 0.801242 | 0.901181 |
| 77 | 0.100000 | 6 | 10 | log_loss | 0.813665 | 0.896209 |
| 78 | 0.100000 | 6 | 50 | gini | 0.875776 | 0.947172 |
| 79 | 0.100000 | 6 | 50 | entropy | 0.819876 | 0.927284 |
| 80 | 0.100000 | 6 | 50 | log_loss | 0.819876 | 0.929770 |
| 81 | 0.100000 | 6 | 100 | gini | 0.894410 | 0.950901 |
| 82 | 0.100000 | 6 | 100 | entropy | 0.875776 | 0.942200 |
| 83 | 0.100000 | 6 | 100 | log_loss | 0.832298 | 0.934121 |
| 84 | 0.100000 | 6 | 200 | gini | 0.863354 | 0.944065 |
| 85 | 0.100000 | 6 | 200 | entropy | 0.844720 | 0.935985 |
| 86 | 0.100000 | 6 | 200 | log_loss | 0.850932 | 0.941579 |
| 87 | 0.100000 | 6 | 300 | gini | 0.881988 | 0.945308 |
| 88 | 0.100000 | 6 | 300 | entropy | 0.857143 | 0.943443 |
| 89 | 0.100000 | 6 | 300 | log_loss | 0.850932 | 0.943443 |
| 90 | 0.100000 | 7 | 10 | gini | 0.850932 | 0.935985 |
| 91 | 0.100000 | 7 | 10 | entropy | 0.850932 | 0.929149 |
| 92 | 0.100000 | 7 | 10 | log_loss | 0.739130 | 0.914232 |
| 93 | 0.100000 | 7 | 50 | gini | 0.881988 | 0.959602 |
| 94 | 0.100000 | 7 | 50 | entropy | 0.857143 | 0.970789 |
| 95 | 0.100000 | 7 | 50 | log_loss | 0.881988 | 0.969546 |
| 96 | 0.100000 | 7 | 100 | gini | 0.900621 | 0.965817 |
| 97 | 0.100000 | 7 | 100 | entropy | 0.857143 | 0.964574 |
| 98 | 0.100000 | 7 | 100 | log_loss | 0.869565 | 0.967682 |
| 99 | 0.100000 | 7 | 200 | gini | 0.863354 | 0.965196 |
| 100 | 0.100000 | 7 | 200 | entropy | 0.850932 | 0.967060 |
| 101 | 0.100000 | 7 | 200 | log_loss | 0.850932 | 0.968925 |
| 102 | 0.100000 | 7 | 300 | gini | 0.869565 | 0.967060 |
| 103 | 0.100000 | 7 | 300 | entropy | 0.869565 | 0.969546 |
| 104 | 0.100000 | 7 | 300 | log_loss | 0.863354 | 0.968925 |
| 105 | 0.100000 | 8 | 10 | gini | 0.813665 | 0.943443 |
| 106 | 0.100000 | 8 | 10 | entropy | 0.838509 | 0.956495 |
| 107 | 0.100000 | 8 | 10 | log_loss | 0.857143 | 0.949037 |
| 108 | 0.100000 | 8 | 50 | gini | 0.869565 | 0.971411 |
| 109 | 0.100000 | 8 | 50 | entropy | 0.844720 | 0.977004 |
| 110 | 0.100000 | 8 | 50 | log_loss | 0.888199 | 0.983841 |
| 111 | 0.100000 | 8 | 100 | gini | 0.857143 | 0.978869 |
| 112 | 0.100000 | 8 | 100 | entropy | 0.857143 | 0.980733 |
| 113 | 0.100000 | 8 | 100 | log_loss | 0.857143 | 0.978869 |
| 114 | 0.100000 | 8 | 200 | gini | 0.881988 | 0.981976 |
| 115 | 0.100000 | 8 | 200 | entropy | 0.869565 | 0.984462 |
| 116 | 0.100000 | 8 | 200 | log_loss | 0.875776 | 0.981355 |
| 117 | 0.100000 | 8 | 300 | gini | 0.894410 | 0.982598 |
| 118 | 0.100000 | 8 | 300 | entropy | 0.857143 | 0.983219 |
| 119 | 0.100000 | 8 | 300 | log_loss | 0.857143 | 0.982598 |
| 120 | 0.100000 | 9 | 10 | gini | 0.838509 | 0.965817 |
| 121 | 0.100000 | 9 | 10 | entropy | 0.782609 | 0.967682 |
| 122 | 0.100000 | 9 | 10 | log_loss | 0.770186 | 0.960845 |
| 123 | 0.100000 | 9 | 50 | gini | 0.900621 | 0.986327 |
| 124 | 0.100000 | 9 | 50 | entropy | 0.850932 | 0.982598 |
| 125 | 0.100000 | 9 | 50 | log_loss | 0.888199 | 0.985705 |
| 126 | 0.100000 | 9 | 100 | gini | 0.900621 | 0.984462 |
| 127 | 0.100000 | 9 | 100 | entropy | 0.875776 | 0.985705 |
| 128 | 0.100000 | 9 | 100 | log_loss | 0.875776 | 0.986327 |
| 129 | 0.100000 | 9 | 200 | gini | 0.900621 | 0.985705 |
| 130 | 0.100000 | 9 | 200 | entropy | 0.894410 | 0.988813 |
| 131 | 0.100000 | 9 | 200 | log_loss | 0.900621 | 0.988813 |
| 132 | 0.100000 | 9 | 300 | gini | 0.906832 | 0.988813 |
| 133 | 0.100000 | 9 | 300 | entropy | 0.875776 | 0.987570 |
| 134 | 0.100000 | 9 | 300 | log_loss | 0.900621 | 0.990056 |
| 135 | 0.100000 | 10 | 10 | gini | 0.813665 | 0.968303 |
| 136 | 0.100000 | 10 | 10 | entropy | 0.801242 | 0.970168 |
| 137 | 0.100000 | 10 | 10 | log_loss | 0.819876 | 0.967682 |
| 138 | 0.100000 | 10 | 50 | gini | 0.906832 | 0.990677 |
| 139 | 0.100000 | 10 | 50 | entropy | 0.888199 | 0.988813 |
| 140 | 0.100000 | 10 | 50 | log_loss | 0.888199 | 0.986948 |
| 141 | 0.100000 | 10 | 100 | gini | 0.925466 | 0.991920 |
| 142 | 0.100000 | 10 | 100 | entropy | 0.881988 | 0.988191 |
| 143 | 0.100000 | 10 | 100 | log_loss | 0.888199 | 0.988191 |
| 144 | 0.100000 | 10 | 200 | gini | 0.900621 | 0.988813 |
| 145 | 0.100000 | 10 | 200 | entropy | 0.900621 | 0.990056 |
| 146 | 0.100000 | 10 | 200 | log_loss | 0.881988 | 0.988191 |
| 147 | 0.100000 | 10 | 300 | gini | 0.913043 | 0.991299 |
| 148 | 0.100000 | 10 | 300 | entropy | 0.869565 | 0.986948 |
| 149 | 0.100000 | 10 | 300 | log_loss | 0.875776 | 0.987570 |
| 150 | 0.100000 | 11 | 10 | gini | 0.838509 | 0.972032 |
| 151 | 0.100000 | 11 | 10 | entropy | 0.776398 | 0.971411 |
| 152 | 0.100000 | 11 | 10 | log_loss | 0.795031 | 0.975140 |
| 153 | 0.100000 | 11 | 50 | gini | 0.875776 | 0.987570 |
| 154 | 0.100000 | 11 | 50 | entropy | 0.869565 | 0.986948 |
| 155 | 0.100000 | 11 | 50 | log_loss | 0.863354 | 0.986327 |
| 156 | 0.100000 | 11 | 100 | gini | 0.881988 | 0.988191 |
| 157 | 0.100000 | 11 | 100 | entropy | 0.881988 | 0.988191 |
| 158 | 0.100000 | 11 | 100 | log_loss | 0.906832 | 0.990677 |
| 159 | 0.100000 | 11 | 200 | gini | 0.900621 | 0.990056 |
| 160 | 0.100000 | 11 | 200 | entropy | 0.894410 | 0.989434 |
| 161 | 0.100000 | 11 | 200 | log_loss | 0.894410 | 0.989434 |
| 162 | 0.100000 | 11 | 300 | gini | 0.900621 | 0.990056 |
| 163 | 0.100000 | 11 | 300 | entropy | 0.900621 | 0.990056 |
| 164 | 0.100000 | 11 | 300 | log_loss | 0.888199 | 0.988813 |
| 165 | 0.100000 | 12 | 10 | gini | 0.782609 | 0.973897 |
| 166 | 0.100000 | 12 | 10 | entropy | 0.850932 | 0.979490 |
| 167 | 0.100000 | 12 | 10 | log_loss | 0.832298 | 0.975761 |
| 168 | 0.100000 | 12 | 50 | gini | 0.881988 | 0.988191 |
| 169 | 0.100000 | 12 | 50 | entropy | 0.888199 | 0.988813 |
| 170 | 0.100000 | 12 | 50 | log_loss | 0.875776 | 0.987570 |
| 171 | 0.100000 | 12 | 100 | gini | 0.894410 | 0.989434 |
| 172 | 0.100000 | 12 | 100 | entropy | 0.863354 | 0.986327 |
| 173 | 0.100000 | 12 | 100 | log_loss | 0.863354 | 0.986327 |
| 174 | 0.100000 | 12 | 200 | gini | 0.906832 | 0.990677 |
| 175 | 0.100000 | 12 | 200 | entropy | 0.881988 | 0.988191 |
| 176 | 0.100000 | 12 | 200 | log_loss | 0.857143 | 0.985705 |
| 177 | 0.100000 | 12 | 300 | gini | 0.894410 | 0.989434 |
| 178 | 0.100000 | 12 | 300 | entropy | 0.888199 | 0.988813 |
| 179 | 0.100000 | 12 | 300 | log_loss | 0.875776 | 0.987570 |
| 180 | 0.100000 | 13 | 10 | gini | 0.757764 | 0.972032 |
| 181 | 0.100000 | 13 | 10 | entropy | 0.819876 | 0.976383 |
| 182 | 0.100000 | 13 | 10 | log_loss | 0.776398 | 0.974518 |
| 183 | 0.100000 | 13 | 50 | gini | 0.875776 | 0.987570 |
| 184 | 0.100000 | 13 | 50 | entropy | 0.894410 | 0.989434 |
| 185 | 0.100000 | 13 | 50 | log_loss | 0.894410 | 0.989434 |
| 186 | 0.100000 | 13 | 100 | gini | 0.888199 | 0.988813 |
| 187 | 0.100000 | 13 | 100 | entropy | 0.906832 | 0.990677 |
| 188 | 0.100000 | 13 | 100 | log_loss | 0.875776 | 0.987570 |
| 189 | 0.100000 | 13 | 200 | gini | 0.900621 | 0.990056 |
| 190 | 0.100000 | 13 | 200 | entropy | 0.881988 | 0.988191 |
| 191 | 0.100000 | 13 | 200 | log_loss | 0.906832 | 0.990677 |
| 192 | 0.100000 | 13 | 300 | gini | 0.913043 | 0.991299 |
| 193 | 0.100000 | 13 | 300 | entropy | 0.894410 | 0.989434 |
| 194 | 0.100000 | 13 | 300 | log_loss | 0.875776 | 0.987570 |
| 195 | 0.100000 | 14 | 10 | gini | 0.857143 | 0.980733 |
| 196 | 0.100000 | 14 | 10 | entropy | 0.807453 | 0.973275 |
| 197 | 0.100000 | 14 | 10 | log_loss | 0.782609 | 0.974518 |
| 198 | 0.100000 | 14 | 50 | gini | 0.919255 | 0.991920 |
| 199 | 0.100000 | 14 | 50 | entropy | 0.869565 | 0.986948 |
| 200 | 0.100000 | 14 | 50 | log_loss | 0.863354 | 0.986327 |
| 201 | 0.100000 | 14 | 100 | gini | 0.900621 | 0.990056 |
| 202 | 0.100000 | 14 | 100 | entropy | 0.875776 | 0.987570 |
| 203 | 0.100000 | 14 | 100 | log_loss | 0.900621 | 0.990056 |
| 204 | 0.100000 | 14 | 200 | gini | 0.919255 | 0.991920 |
| 205 | 0.100000 | 14 | 200 | entropy | 0.906832 | 0.990677 |
| 206 | 0.100000 | 14 | 200 | log_loss | 0.888199 | 0.988813 |
| 207 | 0.100000 | 14 | 300 | gini | 0.894410 | 0.989434 |
| 208 | 0.100000 | 14 | 300 | entropy | 0.894410 | 0.989434 |
| 209 | 0.100000 | 14 | 300 | log_loss | 0.894410 | 0.989434 |
| 210 | 0.100000 | 15 | 10 | gini | 0.826087 | 0.980733 |
| 211 | 0.100000 | 15 | 10 | entropy | 0.832298 | 0.980112 |
| 212 | 0.100000 | 15 | 10 | log_loss | 0.832298 | 0.980733 |
| 213 | 0.100000 | 15 | 50 | gini | 0.906832 | 0.990677 |
| 214 | 0.100000 | 15 | 50 | entropy | 0.894410 | 0.989434 |
| 215 | 0.100000 | 15 | 50 | log_loss | 0.857143 | 0.985705 |
| 216 | 0.100000 | 15 | 100 | gini | 0.931677 | 0.993163 |
| 217 | 0.100000 | 15 | 100 | entropy | 0.875776 | 0.987570 |
| 218 | 0.100000 | 15 | 100 | log_loss | 0.894410 | 0.989434 |
| 219 | 0.100000 | 15 | 200 | gini | 0.913043 | 0.991299 |
| 220 | 0.100000 | 15 | 200 | entropy | 0.875776 | 0.987570 |
| 221 | 0.100000 | 15 | 200 | log_loss | 0.875776 | 0.987570 |
| 222 | 0.100000 | 15 | 300 | gini | 0.900621 | 0.990056 |
| 223 | 0.100000 | 15 | 300 | entropy | 0.881988 | 0.988191 |
| 224 | 0.100000 | 15 | 300 | log_loss | 0.888199 | 0.988813 |
| 225 | 0.100000 | 16 | 10 | gini | 0.826087 | 0.977626 |
| 226 | 0.100000 | 16 | 10 | entropy | 0.732919 | 0.972654 |
| 227 | 0.100000 | 16 | 10 | log_loss | 0.844720 | 0.981976 |
| 228 | 0.100000 | 16 | 50 | gini | 0.919255 | 0.991920 |
| 229 | 0.100000 | 16 | 50 | entropy | 0.857143 | 0.985705 |
| 230 | 0.100000 | 16 | 50 | log_loss | 0.906832 | 0.990677 |
| 231 | 0.100000 | 16 | 100 | gini | 0.913043 | 0.991299 |
| 232 | 0.100000 | 16 | 100 | entropy | 0.900621 | 0.990056 |
| 233 | 0.100000 | 16 | 100 | log_loss | 0.881988 | 0.988191 |
| 234 | 0.100000 | 16 | 200 | gini | 0.900621 | 0.990056 |
| 235 | 0.100000 | 16 | 200 | entropy | 0.881988 | 0.988191 |
| 236 | 0.100000 | 16 | 200 | log_loss | 0.869565 | 0.986948 |
| 237 | 0.100000 | 16 | 300 | gini | 0.919255 | 0.991920 |
| 238 | 0.100000 | 16 | 300 | entropy | 0.888199 | 0.988813 |
| 239 | 0.100000 | 16 | 300 | log_loss | 0.888199 | 0.988813 |
| 240 | 0.100000 | 17 | 10 | gini | 0.881988 | 0.982598 |
| 241 | 0.100000 | 17 | 10 | entropy | 0.826087 | 0.977004 |
| 242 | 0.100000 | 17 | 10 | log_loss | 0.826087 | 0.980112 |
| 243 | 0.100000 | 17 | 50 | gini | 0.906832 | 0.990677 |
| 244 | 0.100000 | 17 | 50 | entropy | 0.850932 | 0.985084 |
| 245 | 0.100000 | 17 | 50 | log_loss | 0.875776 | 0.987570 |
| 246 | 0.100000 | 17 | 100 | gini | 0.919255 | 0.991920 |
| 247 | 0.100000 | 17 | 100 | entropy | 0.888199 | 0.988813 |
| 248 | 0.100000 | 17 | 100 | log_loss | 0.875776 | 0.987570 |
| 249 | 0.100000 | 17 | 200 | gini | 0.888199 | 0.988813 |
| 250 | 0.100000 | 17 | 200 | entropy | 0.888199 | 0.988813 |
| 251 | 0.100000 | 17 | 200 | log_loss | 0.875776 | 0.987570 |
| 252 | 0.100000 | 17 | 300 | gini | 0.888199 | 0.988813 |
| 253 | 0.100000 | 17 | 300 | entropy | 0.900621 | 0.990056 |
| 254 | 0.100000 | 17 | 300 | log_loss | 0.869565 | 0.986948 |
| 255 | 0.100000 | 18 | 10 | gini | 0.801242 | 0.977626 |
| 256 | 0.100000 | 18 | 10 | entropy | 0.826087 | 0.977004 |
| 257 | 0.100000 | 18 | 10 | log_loss | 0.826087 | 0.980733 |
| 258 | 0.100000 | 18 | 50 | gini | 0.913043 | 0.991299 |
| 259 | 0.100000 | 18 | 50 | entropy | 0.863354 | 0.986327 |
| 260 | 0.100000 | 18 | 50 | log_loss | 0.913043 | 0.991299 |
| 261 | 0.100000 | 18 | 100 | gini | 0.894410 | 0.989434 |
| 262 | 0.100000 | 18 | 100 | entropy | 0.863354 | 0.986327 |
| 263 | 0.100000 | 18 | 100 | log_loss | 0.875776 | 0.987570 |
| 264 | 0.100000 | 18 | 200 | gini | 0.894410 | 0.989434 |
| 265 | 0.100000 | 18 | 200 | entropy | 0.888199 | 0.988813 |
| 266 | 0.100000 | 18 | 200 | log_loss | 0.875776 | 0.987570 |
| 267 | 0.100000 | 18 | 300 | gini | 0.900621 | 0.990056 |
| 268 | 0.100000 | 18 | 300 | entropy | 0.888199 | 0.988813 |
| 269 | 0.100000 | 18 | 300 | log_loss | 0.900621 | 0.990056 |
| 270 | 0.100000 | 19 | 10 | gini | 0.770186 | 0.973275 |
| 271 | 0.100000 | 19 | 10 | entropy | 0.819876 | 0.978247 |
| 272 | 0.100000 | 19 | 10 | log_loss | 0.826087 | 0.980733 |
| 273 | 0.100000 | 19 | 50 | gini | 0.869565 | 0.986948 |
| 274 | 0.100000 | 19 | 50 | entropy | 0.894410 | 0.989434 |
| 275 | 0.100000 | 19 | 50 | log_loss | 0.863354 | 0.986327 |
| 276 | 0.100000 | 19 | 100 | gini | 0.894410 | 0.989434 |
| 277 | 0.100000 | 19 | 100 | entropy | 0.900621 | 0.990056 |
| 278 | 0.100000 | 19 | 100 | log_loss | 0.894410 | 0.989434 |
| 279 | 0.100000 | 19 | 200 | gini | 0.888199 | 0.988813 |
| 280 | 0.100000 | 19 | 200 | entropy | 0.894410 | 0.989434 |
| 281 | 0.100000 | 19 | 200 | log_loss | 0.900621 | 0.990056 |
| 282 | 0.100000 | 19 | 300 | gini | 0.906832 | 0.990677 |
| 283 | 0.100000 | 19 | 300 | entropy | 0.869565 | 0.986948 |
| 284 | 0.100000 | 19 | 300 | log_loss | 0.906832 | 0.990677 |
| 285 | 0.100000 | 20 | 10 | gini | 0.776398 | 0.971411 |
| 286 | 0.100000 | 20 | 10 | entropy | 0.763975 | 0.974518 |
| 287 | 0.100000 | 20 | 10 | log_loss | 0.763975 | 0.975761 |
| 288 | 0.100000 | 20 | 50 | gini | 0.906832 | 0.990677 |
| 289 | 0.100000 | 20 | 50 | entropy | 0.857143 | 0.985705 |
| 290 | 0.100000 | 20 | 50 | log_loss | 0.857143 | 0.985705 |
| 291 | 0.100000 | 20 | 100 | gini | 0.900621 | 0.990056 |
| 292 | 0.100000 | 20 | 100 | entropy | 0.869565 | 0.986948 |
| 293 | 0.100000 | 20 | 100 | log_loss | 0.875776 | 0.987570 |
| 294 | 0.100000 | 20 | 200 | gini | 0.925466 | 0.992542 |
| 295 | 0.100000 | 20 | 200 | entropy | 0.906832 | 0.990677 |
| 296 | 0.100000 | 20 | 200 | log_loss | 0.900621 | 0.990056 |
| 297 | 0.100000 | 20 | 300 | gini | 0.900621 | 0.990056 |
| 298 | 0.100000 | 20 | 300 | entropy | 0.881988 | 0.988191 |
| 299 | 0.100000 | 20 | 300 | log_loss | 0.900621 | 0.990056 |
| 300 | 0.150000 | 1 | 10 | gini | 0.338843 | 0.338098 |
| 301 | 0.150000 | 1 | 10 | entropy | 0.578512 | 0.557489 |
| 302 | 0.150000 | 1 | 10 | log_loss | 0.578512 | 0.560597 |
| 303 | 0.150000 | 1 | 50 | gini | 0.710744 | 0.681790 |
| 304 | 0.150000 | 1 | 50 | entropy | 0.615702 | 0.590429 |
| 305 | 0.150000 | 1 | 50 | log_loss | 0.615702 | 0.584835 |
| 306 | 0.150000 | 1 | 100 | gini | 0.615702 | 0.651958 |
| 307 | 0.150000 | 1 | 100 | entropy | 0.582645 | 0.588564 |
| 308 | 0.150000 | 1 | 100 | log_loss | 0.603306 | 0.577377 |
| 309 | 0.150000 | 1 | 200 | gini | 0.652893 | 0.669360 |
| 310 | 0.150000 | 1 | 200 | entropy | 0.607438 | 0.581106 |
| 311 | 0.150000 | 1 | 200 | log_loss | 0.586777 | 0.569919 |
| 312 | 0.150000 | 1 | 300 | gini | 0.681818 | 0.677439 |
| 313 | 0.150000 | 1 | 300 | entropy | 0.603306 | 0.595401 |
| 314 | 0.150000 | 1 | 300 | log_loss | 0.595041 | 0.578620 |
| 315 | 0.150000 | 2 | 10 | gini | 0.681818 | 0.743319 |
| 316 | 0.150000 | 2 | 10 | entropy | 0.504132 | 0.541952 |
| 317 | 0.150000 | 2 | 10 | log_loss | 0.524793 | 0.550031 |
| 318 | 0.150000 | 2 | 50 | gini | 0.760331 | 0.764450 |
| 319 | 0.150000 | 2 | 50 | entropy | 0.747934 | 0.766314 |
| 320 | 0.150000 | 2 | 50 | log_loss | 0.681818 | 0.715351 |
| 321 | 0.150000 | 2 | 100 | gini | 0.739669 | 0.749534 |
| 322 | 0.150000 | 2 | 100 | entropy | 0.706612 | 0.711001 |
| 323 | 0.150000 | 2 | 100 | log_loss | 0.702479 | 0.720323 |
| 324 | 0.150000 | 2 | 200 | gini | 0.818182 | 0.825979 |
| 325 | 0.150000 | 2 | 200 | entropy | 0.743802 | 0.748912 |
| 326 | 0.150000 | 2 | 200 | log_loss | 0.776860 | 0.765071 |
| 327 | 0.150000 | 2 | 300 | gini | 0.789256 | 0.809820 |
| 328 | 0.150000 | 2 | 300 | entropy | 0.719008 | 0.704786 |
| 329 | 0.150000 | 2 | 300 | log_loss | 0.723140 | 0.737104 |
| 330 | 0.150000 | 3 | 10 | gini | 0.768595 | 0.788689 |
| 331 | 0.150000 | 3 | 10 | entropy | 0.735537 | 0.717837 |
| 332 | 0.150000 | 3 | 10 | log_loss | 0.735537 | 0.732132 |
| 333 | 0.150000 | 3 | 50 | gini | 0.826446 | 0.832194 |
| 334 | 0.150000 | 3 | 50 | entropy | 0.752066 | 0.759478 |
| 335 | 0.150000 | 3 | 50 | log_loss | 0.801653 | 0.815413 |
| 336 | 0.150000 | 3 | 100 | gini | 0.822314 | 0.860162 |
| 337 | 0.150000 | 3 | 100 | entropy | 0.805785 | 0.823493 |
| 338 | 0.150000 | 3 | 100 | log_loss | 0.776860 | 0.823493 |
| 339 | 0.150000 | 3 | 200 | gini | 0.838843 | 0.852082 |
| 340 | 0.150000 | 3 | 200 | entropy | 0.805785 | 0.831572 |
| 341 | 0.150000 | 3 | 200 | log_loss | 0.809917 | 0.831572 |
| 342 | 0.150000 | 3 | 300 | gini | 0.822314 | 0.850218 |
| 343 | 0.150000 | 3 | 300 | entropy | 0.801653 | 0.837166 |
| 344 | 0.150000 | 3 | 300 | log_loss | 0.818182 | 0.848353 |
| 345 | 0.150000 | 4 | 10 | gini | 0.760331 | 0.793661 |
| 346 | 0.150000 | 4 | 10 | entropy | 0.826446 | 0.832194 |
| 347 | 0.150000 | 4 | 10 | log_loss | 0.814050 | 0.828465 |
| 348 | 0.150000 | 4 | 50 | gini | 0.834711 | 0.875078 |
| 349 | 0.150000 | 4 | 50 | entropy | 0.814050 | 0.858919 |
| 350 | 0.150000 | 4 | 50 | log_loss | 0.826446 | 0.868241 |
| 351 | 0.150000 | 4 | 100 | gini | 0.822314 | 0.882536 |
| 352 | 0.150000 | 4 | 100 | entropy | 0.818182 | 0.870106 |
| 353 | 0.150000 | 4 | 100 | log_loss | 0.818182 | 0.869484 |
| 354 | 0.150000 | 4 | 200 | gini | 0.838843 | 0.893723 |
| 355 | 0.150000 | 4 | 200 | entropy | 0.826446 | 0.876942 |
| 356 | 0.150000 | 4 | 200 | log_loss | 0.822314 | 0.875699 |
| 357 | 0.150000 | 4 | 300 | gini | 0.830579 | 0.881293 |
| 358 | 0.150000 | 4 | 300 | entropy | 0.830579 | 0.871349 |
| 359 | 0.150000 | 4 | 300 | log_loss | 0.830579 | 0.876942 |
| 360 | 0.150000 | 5 | 10 | gini | 0.776860 | 0.843381 |
| 361 | 0.150000 | 5 | 10 | entropy | 0.780992 | 0.865134 |
| 362 | 0.150000 | 5 | 10 | log_loss | 0.756198 | 0.814792 |
| 363 | 0.150000 | 5 | 50 | gini | 0.834711 | 0.904288 |
| 364 | 0.150000 | 5 | 50 | entropy | 0.826446 | 0.887508 |
| 365 | 0.150000 | 5 | 50 | log_loss | 0.822314 | 0.896209 |
| 366 | 0.150000 | 5 | 100 | gini | 0.867769 | 0.918583 |
| 367 | 0.150000 | 5 | 100 | entropy | 0.826446 | 0.896830 |
| 368 | 0.150000 | 5 | 100 | log_loss | 0.842975 | 0.903045 |
| 369 | 0.150000 | 5 | 200 | gini | 0.847107 | 0.916718 |
| 370 | 0.150000 | 5 | 200 | entropy | 0.814050 | 0.901802 |
| 371 | 0.150000 | 5 | 200 | log_loss | 0.826446 | 0.899938 |
| 372 | 0.150000 | 5 | 300 | gini | 0.859504 | 0.920447 |
| 373 | 0.150000 | 5 | 300 | entropy | 0.830579 | 0.898073 |
| 374 | 0.150000 | 5 | 300 | log_loss | 0.838843 | 0.900559 |
| 375 | 0.150000 | 6 | 10 | gini | 0.809917 | 0.888129 |
| 376 | 0.150000 | 6 | 10 | entropy | 0.797521 | 0.889372 |
| 377 | 0.150000 | 6 | 10 | log_loss | 0.780992 | 0.900559 |
| 378 | 0.150000 | 6 | 50 | gini | 0.871901 | 0.936607 |
| 379 | 0.150000 | 6 | 50 | entropy | 0.851240 | 0.940336 |
| 380 | 0.150000 | 6 | 50 | log_loss | 0.859504 | 0.932256 |
| 381 | 0.150000 | 6 | 100 | gini | 0.847107 | 0.934121 |
| 382 | 0.150000 | 6 | 100 | entropy | 0.855372 | 0.934121 |
| 383 | 0.150000 | 6 | 100 | log_loss | 0.851240 | 0.935985 |
| 384 | 0.150000 | 6 | 200 | gini | 0.876033 | 0.945929 |
| 385 | 0.150000 | 6 | 200 | entropy | 0.855372 | 0.932256 |
| 386 | 0.150000 | 6 | 200 | log_loss | 0.851240 | 0.937850 |
| 387 | 0.150000 | 6 | 300 | gini | 0.859504 | 0.944686 |
| 388 | 0.150000 | 6 | 300 | entropy | 0.855372 | 0.935985 |
| 389 | 0.150000 | 6 | 300 | log_loss | 0.867769 | 0.939714 |
| 390 | 0.150000 | 7 | 10 | gini | 0.814050 | 0.913611 |
| 391 | 0.150000 | 7 | 10 | entropy | 0.809917 | 0.931013 |
| 392 | 0.150000 | 7 | 10 | log_loss | 0.863636 | 0.927906 |
| 393 | 0.150000 | 7 | 50 | gini | 0.867769 | 0.954630 |
| 394 | 0.150000 | 7 | 50 | entropy | 0.855372 | 0.954630 |
| 395 | 0.150000 | 7 | 50 | log_loss | 0.851240 | 0.955252 |
| 396 | 0.150000 | 7 | 100 | gini | 0.871901 | 0.966439 |
| 397 | 0.150000 | 7 | 100 | entropy | 0.842975 | 0.959602 |
| 398 | 0.150000 | 7 | 100 | log_loss | 0.863636 | 0.960845 |
| 399 | 0.150000 | 7 | 200 | gini | 0.888430 | 0.965196 |
| 400 | 0.150000 | 7 | 200 | entropy | 0.871901 | 0.969546 |
| 401 | 0.150000 | 7 | 200 | log_loss | 0.863636 | 0.958981 |
| 402 | 0.150000 | 7 | 300 | gini | 0.867769 | 0.962088 |
| 403 | 0.150000 | 7 | 300 | entropy | 0.871901 | 0.964574 |
| 404 | 0.150000 | 7 | 300 | log_loss | 0.851240 | 0.961467 |
| 405 | 0.150000 | 8 | 10 | gini | 0.859504 | 0.954009 |
| 406 | 0.150000 | 8 | 10 | entropy | 0.772727 | 0.937850 |
| 407 | 0.150000 | 8 | 10 | log_loss | 0.756198 | 0.919204 |
| 408 | 0.150000 | 8 | 50 | gini | 0.871901 | 0.974518 |
| 409 | 0.150000 | 8 | 50 | entropy | 0.855372 | 0.972032 |
| 410 | 0.150000 | 8 | 50 | log_loss | 0.880165 | 0.972654 |
| 411 | 0.150000 | 8 | 100 | gini | 0.863636 | 0.971411 |
| 412 | 0.150000 | 8 | 100 | entropy | 0.855372 | 0.970789 |
| 413 | 0.150000 | 8 | 100 | log_loss | 0.884298 | 0.979490 |
| 414 | 0.150000 | 8 | 200 | gini | 0.900826 | 0.979490 |
| 415 | 0.150000 | 8 | 200 | entropy | 0.876033 | 0.975140 |
| 416 | 0.150000 | 8 | 200 | log_loss | 0.855372 | 0.974518 |
| 417 | 0.150000 | 8 | 300 | gini | 0.876033 | 0.974518 |
| 418 | 0.150000 | 8 | 300 | entropy | 0.859504 | 0.977004 |
| 419 | 0.150000 | 8 | 300 | log_loss | 0.867769 | 0.977626 |
| 420 | 0.150000 | 9 | 10 | gini | 0.814050 | 0.949037 |
| 421 | 0.150000 | 9 | 10 | entropy | 0.859504 | 0.961467 |
| 422 | 0.150000 | 9 | 10 | log_loss | 0.809917 | 0.960224 |
| 423 | 0.150000 | 9 | 50 | gini | 0.859504 | 0.973897 |
| 424 | 0.150000 | 9 | 50 | entropy | 0.863636 | 0.977004 |
| 425 | 0.150000 | 9 | 50 | log_loss | 0.925620 | 0.988191 |
| 426 | 0.150000 | 9 | 100 | gini | 0.896694 | 0.981976 |
| 427 | 0.150000 | 9 | 100 | entropy | 0.855372 | 0.977004 |
| 428 | 0.150000 | 9 | 100 | log_loss | 0.880165 | 0.981976 |
| 429 | 0.150000 | 9 | 200 | gini | 0.888430 | 0.981355 |
| 430 | 0.150000 | 9 | 200 | entropy | 0.880165 | 0.981976 |
| 431 | 0.150000 | 9 | 200 | log_loss | 0.863636 | 0.978869 |
| 432 | 0.150000 | 9 | 300 | gini | 0.871901 | 0.979490 |
| 433 | 0.150000 | 9 | 300 | entropy | 0.855372 | 0.977626 |
| 434 | 0.150000 | 9 | 300 | log_loss | 0.876033 | 0.980733 |
| 435 | 0.150000 | 10 | 10 | gini | 0.772727 | 0.955873 |
| 436 | 0.150000 | 10 | 10 | entropy | 0.830579 | 0.968925 |
| 437 | 0.150000 | 10 | 10 | log_loss | 0.809917 | 0.961467 |
| 438 | 0.150000 | 10 | 50 | gini | 0.867769 | 0.979490 |
| 439 | 0.150000 | 10 | 50 | entropy | 0.876033 | 0.981355 |
| 440 | 0.150000 | 10 | 50 | log_loss | 0.892562 | 0.983841 |
| 441 | 0.150000 | 10 | 100 | gini | 0.888430 | 0.982598 |
| 442 | 0.150000 | 10 | 100 | entropy | 0.888430 | 0.983219 |
| 443 | 0.150000 | 10 | 100 | log_loss | 0.863636 | 0.979490 |
| 444 | 0.150000 | 10 | 200 | gini | 0.880165 | 0.981976 |
| 445 | 0.150000 | 10 | 200 | entropy | 0.880165 | 0.981976 |
| 446 | 0.150000 | 10 | 200 | log_loss | 0.900826 | 0.985084 |
| 447 | 0.150000 | 10 | 300 | gini | 0.880165 | 0.981355 |
| 448 | 0.150000 | 10 | 300 | entropy | 0.880165 | 0.981976 |
| 449 | 0.150000 | 10 | 300 | log_loss | 0.888430 | 0.983219 |
| 450 | 0.150000 | 11 | 10 | gini | 0.859504 | 0.965817 |
| 451 | 0.150000 | 11 | 10 | entropy | 0.830579 | 0.969546 |
| 452 | 0.150000 | 11 | 10 | log_loss | 0.822314 | 0.964574 |
| 453 | 0.150000 | 11 | 50 | gini | 0.876033 | 0.981355 |
| 454 | 0.150000 | 11 | 50 | entropy | 0.867769 | 0.980112 |
| 455 | 0.150000 | 11 | 50 | log_loss | 0.888430 | 0.983219 |
| 456 | 0.150000 | 11 | 100 | gini | 0.917355 | 0.987570 |
| 457 | 0.150000 | 11 | 100 | entropy | 0.888430 | 0.983219 |
| 458 | 0.150000 | 11 | 100 | log_loss | 0.888430 | 0.983219 |
| 459 | 0.150000 | 11 | 200 | gini | 0.904959 | 0.985705 |
| 460 | 0.150000 | 11 | 200 | entropy | 0.876033 | 0.981355 |
| 461 | 0.150000 | 11 | 200 | log_loss | 0.896694 | 0.984462 |
| 462 | 0.150000 | 11 | 300 | gini | 0.892562 | 0.983841 |
| 463 | 0.150000 | 11 | 300 | entropy | 0.880165 | 0.981976 |
| 464 | 0.150000 | 11 | 300 | log_loss | 0.888430 | 0.983219 |
| 465 | 0.150000 | 12 | 10 | gini | 0.818182 | 0.970789 |
| 466 | 0.150000 | 12 | 10 | entropy | 0.863636 | 0.975761 |
| 467 | 0.150000 | 12 | 10 | log_loss | 0.863636 | 0.973897 |
| 468 | 0.150000 | 12 | 50 | gini | 0.884298 | 0.982598 |
| 469 | 0.150000 | 12 | 50 | entropy | 0.867769 | 0.980112 |
| 470 | 0.150000 | 12 | 50 | log_loss | 0.855372 | 0.978247 |
| 471 | 0.150000 | 12 | 100 | gini | 0.871901 | 0.980733 |
| 472 | 0.150000 | 12 | 100 | entropy | 0.904959 | 0.985705 |
| 473 | 0.150000 | 12 | 100 | log_loss | 0.880165 | 0.981976 |
| 474 | 0.150000 | 12 | 200 | gini | 0.892562 | 0.983841 |
| 475 | 0.150000 | 12 | 200 | entropy | 0.888430 | 0.983219 |
| 476 | 0.150000 | 12 | 200 | log_loss | 0.884298 | 0.982598 |
| 477 | 0.150000 | 12 | 300 | gini | 0.896694 | 0.984462 |
| 478 | 0.150000 | 12 | 300 | entropy | 0.892562 | 0.983841 |
| 479 | 0.150000 | 12 | 300 | log_loss | 0.871901 | 0.980733 |
| 480 | 0.150000 | 13 | 10 | gini | 0.847107 | 0.973897 |
| 481 | 0.150000 | 13 | 10 | entropy | 0.818182 | 0.968925 |
| 482 | 0.150000 | 13 | 10 | log_loss | 0.801653 | 0.963331 |
| 483 | 0.150000 | 13 | 50 | gini | 0.900826 | 0.985084 |
| 484 | 0.150000 | 13 | 50 | entropy | 0.863636 | 0.979490 |
| 485 | 0.150000 | 13 | 50 | log_loss | 0.892562 | 0.983841 |
| 486 | 0.150000 | 13 | 100 | gini | 0.884298 | 0.982598 |
| 487 | 0.150000 | 13 | 100 | entropy | 0.884298 | 0.982598 |
| 488 | 0.150000 | 13 | 100 | log_loss | 0.900826 | 0.985084 |
| 489 | 0.150000 | 13 | 200 | gini | 0.896694 | 0.984462 |
| 490 | 0.150000 | 13 | 200 | entropy | 0.867769 | 0.980112 |
| 491 | 0.150000 | 13 | 200 | log_loss | 0.871901 | 0.980733 |
| 492 | 0.150000 | 13 | 300 | gini | 0.888430 | 0.983219 |
| 493 | 0.150000 | 13 | 300 | entropy | 0.880165 | 0.981976 |
| 494 | 0.150000 | 13 | 300 | log_loss | 0.888430 | 0.983219 |
| 495 | 0.150000 | 14 | 10 | gini | 0.809917 | 0.965817 |
| 496 | 0.150000 | 14 | 10 | entropy | 0.834711 | 0.971411 |
| 497 | 0.150000 | 14 | 10 | log_loss | 0.793388 | 0.966439 |
| 498 | 0.150000 | 14 | 50 | gini | 0.900826 | 0.985084 |
| 499 | 0.150000 | 14 | 50 | entropy | 0.859504 | 0.978869 |
| 500 | 0.150000 | 14 | 50 | log_loss | 0.880165 | 0.981976 |
| 501 | 0.150000 | 14 | 100 | gini | 0.880165 | 0.981976 |
| 502 | 0.150000 | 14 | 100 | entropy | 0.904959 | 0.985705 |
| 503 | 0.150000 | 14 | 100 | log_loss | 0.876033 | 0.981355 |
| 504 | 0.150000 | 14 | 200 | gini | 0.900826 | 0.985084 |
| 505 | 0.150000 | 14 | 200 | entropy | 0.863636 | 0.979490 |
| 506 | 0.150000 | 14 | 200 | log_loss | 0.888430 | 0.983219 |
| 507 | 0.150000 | 14 | 300 | gini | 0.909091 | 0.986327 |
| 508 | 0.150000 | 14 | 300 | entropy | 0.896694 | 0.984462 |
| 509 | 0.150000 | 14 | 300 | log_loss | 0.888430 | 0.983219 |
| 510 | 0.150000 | 15 | 10 | gini | 0.785124 | 0.965817 |
| 511 | 0.150000 | 15 | 10 | entropy | 0.785124 | 0.963953 |
| 512 | 0.150000 | 15 | 10 | log_loss | 0.814050 | 0.969546 |
| 513 | 0.150000 | 15 | 50 | gini | 0.896694 | 0.984462 |
| 514 | 0.150000 | 15 | 50 | entropy | 0.896694 | 0.984462 |
| 515 | 0.150000 | 15 | 50 | log_loss | 0.851240 | 0.977626 |
| 516 | 0.150000 | 15 | 100 | gini | 0.871901 | 0.980733 |
| 517 | 0.150000 | 15 | 100 | entropy | 0.892562 | 0.983841 |
| 518 | 0.150000 | 15 | 100 | log_loss | 0.884298 | 0.982598 |
| 519 | 0.150000 | 15 | 200 | gini | 0.913223 | 0.986948 |
| 520 | 0.150000 | 15 | 200 | entropy | 0.880165 | 0.981976 |
| 521 | 0.150000 | 15 | 200 | log_loss | 0.896694 | 0.984462 |
| 522 | 0.150000 | 15 | 300 | gini | 0.909091 | 0.986327 |
| 523 | 0.150000 | 15 | 300 | entropy | 0.871901 | 0.980733 |
| 524 | 0.150000 | 15 | 300 | log_loss | 0.884298 | 0.982598 |
| 525 | 0.150000 | 16 | 10 | gini | 0.776860 | 0.962088 |
| 526 | 0.150000 | 16 | 10 | entropy | 0.805785 | 0.968925 |
| 527 | 0.150000 | 16 | 10 | log_loss | 0.814050 | 0.966439 |
| 528 | 0.150000 | 16 | 50 | gini | 0.876033 | 0.981355 |
| 529 | 0.150000 | 16 | 50 | entropy | 0.884298 | 0.982598 |
| 530 | 0.150000 | 16 | 50 | log_loss | 0.896694 | 0.984462 |
| 531 | 0.150000 | 16 | 100 | gini | 0.900826 | 0.985084 |
| 532 | 0.150000 | 16 | 100 | entropy | 0.904959 | 0.985705 |
| 533 | 0.150000 | 16 | 100 | log_loss | 0.871901 | 0.980733 |
| 534 | 0.150000 | 16 | 200 | gini | 0.904959 | 0.985705 |
| 535 | 0.150000 | 16 | 200 | entropy | 0.888430 | 0.983219 |
| 536 | 0.150000 | 16 | 200 | log_loss | 0.892562 | 0.983841 |
| 537 | 0.150000 | 16 | 300 | gini | 0.896694 | 0.984462 |
| 538 | 0.150000 | 16 | 300 | entropy | 0.900826 | 0.985084 |
| 539 | 0.150000 | 16 | 300 | log_loss | 0.884298 | 0.982598 |
| 540 | 0.150000 | 17 | 10 | gini | 0.826446 | 0.969546 |
| 541 | 0.150000 | 17 | 10 | entropy | 0.838843 | 0.971411 |
| 542 | 0.150000 | 17 | 10 | log_loss | 0.847107 | 0.973275 |
| 543 | 0.150000 | 17 | 50 | gini | 0.871901 | 0.980733 |
| 544 | 0.150000 | 17 | 50 | entropy | 0.847107 | 0.977004 |
| 545 | 0.150000 | 17 | 50 | log_loss | 0.900826 | 0.985084 |
| 546 | 0.150000 | 17 | 100 | gini | 0.896694 | 0.984462 |
| 547 | 0.150000 | 17 | 100 | entropy | 0.888430 | 0.983219 |
| 548 | 0.150000 | 17 | 100 | log_loss | 0.888430 | 0.983219 |
| 549 | 0.150000 | 17 | 200 | gini | 0.904959 | 0.985705 |
| 550 | 0.150000 | 17 | 200 | entropy | 0.888430 | 0.983219 |
| 551 | 0.150000 | 17 | 200 | log_loss | 0.888430 | 0.983219 |
| 552 | 0.150000 | 17 | 300 | gini | 0.904959 | 0.985705 |
| 553 | 0.150000 | 17 | 300 | entropy | 0.880165 | 0.981976 |
| 554 | 0.150000 | 17 | 300 | log_loss | 0.880165 | 0.981976 |
| 555 | 0.150000 | 18 | 10 | gini | 0.855372 | 0.976383 |
| 556 | 0.150000 | 18 | 10 | entropy | 0.809917 | 0.968925 |
| 557 | 0.150000 | 18 | 10 | log_loss | 0.830579 | 0.972654 |
| 558 | 0.150000 | 18 | 50 | gini | 0.871901 | 0.980733 |
| 559 | 0.150000 | 18 | 50 | entropy | 0.859504 | 0.978869 |
| 560 | 0.150000 | 18 | 50 | log_loss | 0.880165 | 0.981976 |
| 561 | 0.150000 | 18 | 100 | gini | 0.880165 | 0.981976 |
| 562 | 0.150000 | 18 | 100 | entropy | 0.888430 | 0.983219 |
| 563 | 0.150000 | 18 | 100 | log_loss | 0.851240 | 0.977626 |
| 564 | 0.150000 | 18 | 200 | gini | 0.896694 | 0.984462 |
| 565 | 0.150000 | 18 | 200 | entropy | 0.847107 | 0.977004 |
| 566 | 0.150000 | 18 | 200 | log_loss | 0.892562 | 0.983841 |
| 567 | 0.150000 | 18 | 300 | gini | 0.896694 | 0.984462 |
| 568 | 0.150000 | 18 | 300 | entropy | 0.884298 | 0.982598 |
| 569 | 0.150000 | 18 | 300 | log_loss | 0.888430 | 0.983219 |
| 570 | 0.150000 | 19 | 10 | gini | 0.822314 | 0.970789 |
| 571 | 0.150000 | 19 | 10 | entropy | 0.805785 | 0.967682 |
| 572 | 0.150000 | 19 | 10 | log_loss | 0.818182 | 0.968925 |
| 573 | 0.150000 | 19 | 50 | gini | 0.876033 | 0.981355 |
| 574 | 0.150000 | 19 | 50 | entropy | 0.896694 | 0.984462 |
| 575 | 0.150000 | 19 | 50 | log_loss | 0.900826 | 0.985084 |
| 576 | 0.150000 | 19 | 100 | gini | 0.909091 | 0.986327 |
| 577 | 0.150000 | 19 | 100 | entropy | 0.884298 | 0.982598 |
| 578 | 0.150000 | 19 | 100 | log_loss | 0.888430 | 0.983219 |
| 579 | 0.150000 | 19 | 200 | gini | 0.896694 | 0.984462 |
| 580 | 0.150000 | 19 | 200 | entropy | 0.896694 | 0.984462 |
| 581 | 0.150000 | 19 | 200 | log_loss | 0.884298 | 0.982598 |
| 582 | 0.150000 | 19 | 300 | gini | 0.892562 | 0.983841 |
| 583 | 0.150000 | 19 | 300 | entropy | 0.892562 | 0.983841 |
| 584 | 0.150000 | 19 | 300 | log_loss | 0.880165 | 0.981976 |
| 585 | 0.150000 | 20 | 10 | gini | 0.805785 | 0.967682 |
| 586 | 0.150000 | 20 | 10 | entropy | 0.838843 | 0.972654 |
| 587 | 0.150000 | 20 | 10 | log_loss | 0.780992 | 0.963331 |
| 588 | 0.150000 | 20 | 50 | gini | 0.888430 | 0.983219 |
| 589 | 0.150000 | 20 | 50 | entropy | 0.888430 | 0.983219 |
| 590 | 0.150000 | 20 | 50 | log_loss | 0.892562 | 0.983841 |
| 591 | 0.150000 | 20 | 100 | gini | 0.900826 | 0.985084 |
| 592 | 0.150000 | 20 | 100 | entropy | 0.896694 | 0.984462 |
| 593 | 0.150000 | 20 | 100 | log_loss | 0.867769 | 0.980112 |
| 594 | 0.150000 | 20 | 200 | gini | 0.909091 | 0.986327 |
| 595 | 0.150000 | 20 | 200 | entropy | 0.884298 | 0.982598 |
| 596 | 0.150000 | 20 | 200 | log_loss | 0.900826 | 0.985084 |
| 597 | 0.150000 | 20 | 300 | gini | 0.909091 | 0.986327 |
| 598 | 0.150000 | 20 | 300 | entropy | 0.892562 | 0.983841 |
| 599 | 0.150000 | 20 | 300 | log_loss | 0.896694 | 0.984462 |
| 600 | 0.200000 | 1 | 10 | gini | 0.506211 | 0.503418 |
| 601 | 0.200000 | 1 | 10 | entropy | 0.484472 | 0.513984 |
| 602 | 0.200000 | 1 | 10 | log_loss | 0.586957 | 0.568676 |
| 603 | 0.200000 | 1 | 50 | gini | 0.695652 | 0.693599 |
| 604 | 0.200000 | 1 | 50 | entropy | 0.580745 | 0.568055 |
| 605 | 0.200000 | 1 | 50 | log_loss | 0.593168 | 0.568055 |
| 606 | 0.200000 | 1 | 100 | gini | 0.717391 | 0.726538 |
| 607 | 0.200000 | 1 | 100 | entropy | 0.602484 | 0.592293 |
| 608 | 0.200000 | 1 | 100 | log_loss | 0.605590 | 0.586078 |
| 609 | 0.200000 | 1 | 200 | gini | 0.686335 | 0.699814 |
| 610 | 0.200000 | 1 | 200 | entropy | 0.605590 | 0.591050 |
| 611 | 0.200000 | 1 | 200 | log_loss | 0.614907 | 0.591672 |
| 612 | 0.200000 | 1 | 300 | gini | 0.701863 | 0.709758 |
| 613 | 0.200000 | 1 | 300 | entropy | 0.611801 | 0.596022 |
| 614 | 0.200000 | 1 | 300 | log_loss | 0.599379 | 0.582349 |
| 615 | 0.200000 | 2 | 10 | gini | 0.602484 | 0.585457 |
| 616 | 0.200000 | 2 | 10 | entropy | 0.645963 | 0.686762 |
| 617 | 0.200000 | 2 | 10 | log_loss | 0.562112 | 0.545681 |
| 618 | 0.200000 | 2 | 50 | gini | 0.798137 | 0.800497 |
| 619 | 0.200000 | 2 | 50 | entropy | 0.692547 | 0.666252 |
| 620 | 0.200000 | 2 | 50 | log_loss | 0.742236 | 0.738968 |
| 621 | 0.200000 | 2 | 100 | gini | 0.798137 | 0.810441 |
| 622 | 0.200000 | 2 | 100 | entropy | 0.736025 | 0.742076 |
| 623 | 0.200000 | 2 | 100 | log_loss | 0.807453 | 0.789310 |
| 624 | 0.200000 | 2 | 200 | gini | 0.788820 | 0.806712 |
| 625 | 0.200000 | 2 | 200 | entropy | 0.751553 | 0.749534 |
| 626 | 0.200000 | 2 | 200 | log_loss | 0.788820 | 0.784960 |
| 627 | 0.200000 | 2 | 300 | gini | 0.782609 | 0.798011 |
| 628 | 0.200000 | 2 | 300 | entropy | 0.785714 | 0.784338 |
| 629 | 0.200000 | 2 | 300 | log_loss | 0.791925 | 0.801119 |
| 630 | 0.200000 | 3 | 10 | gini | 0.736025 | 0.785581 |
| 631 | 0.200000 | 3 | 10 | entropy | 0.714286 | 0.718459 |
| 632 | 0.200000 | 3 | 10 | log_loss | 0.661491 | 0.675575 |
| 633 | 0.200000 | 3 | 50 | gini | 0.795031 | 0.829086 |
| 634 | 0.200000 | 3 | 50 | entropy | 0.767081 | 0.781852 |
| 635 | 0.200000 | 3 | 50 | log_loss | 0.819876 | 0.836544 |
| 636 | 0.200000 | 3 | 100 | gini | 0.798137 | 0.832815 |
| 637 | 0.200000 | 3 | 100 | entropy | 0.822981 | 0.836544 |
| 638 | 0.200000 | 3 | 100 | log_loss | 0.804348 | 0.830951 |
| 639 | 0.200000 | 3 | 200 | gini | 0.813665 | 0.846489 |
| 640 | 0.200000 | 3 | 200 | entropy | 0.822981 | 0.849596 |
| 641 | 0.200000 | 3 | 200 | log_loss | 0.826087 | 0.844002 |
| 642 | 0.200000 | 3 | 300 | gini | 0.826087 | 0.848353 |
| 643 | 0.200000 | 3 | 300 | entropy | 0.807453 | 0.842138 |
| 644 | 0.200000 | 3 | 300 | log_loss | 0.822981 | 0.845245 |
| 645 | 0.200000 | 4 | 10 | gini | 0.726708 | 0.780609 |
| 646 | 0.200000 | 4 | 10 | entropy | 0.776398 | 0.821628 |
| 647 | 0.200000 | 4 | 10 | log_loss | 0.785714 | 0.812927 |
| 648 | 0.200000 | 4 | 50 | gini | 0.838509 | 0.869484 |
| 649 | 0.200000 | 4 | 50 | entropy | 0.822981 | 0.862648 |
| 650 | 0.200000 | 4 | 50 | log_loss | 0.813665 | 0.870727 |
| 651 | 0.200000 | 4 | 100 | gini | 0.832298 | 0.884400 |
| 652 | 0.200000 | 4 | 100 | entropy | 0.829193 | 0.861405 |
| 653 | 0.200000 | 4 | 100 | log_loss | 0.819876 | 0.863269 |
| 654 | 0.200000 | 4 | 200 | gini | 0.832298 | 0.878185 |
| 655 | 0.200000 | 4 | 200 | entropy | 0.847826 | 0.878807 |
| 656 | 0.200000 | 4 | 200 | log_loss | 0.838509 | 0.875078 |
| 657 | 0.200000 | 4 | 300 | gini | 0.816770 | 0.871970 |
| 658 | 0.200000 | 4 | 300 | entropy | 0.832298 | 0.877564 |
| 659 | 0.200000 | 4 | 300 | log_loss | 0.835404 | 0.870727 |
| 660 | 0.200000 | 5 | 10 | gini | 0.782609 | 0.845867 |
| 661 | 0.200000 | 5 | 10 | entropy | 0.739130 | 0.841516 |
| 662 | 0.200000 | 5 | 10 | log_loss | 0.757764 | 0.836544 |
| 663 | 0.200000 | 5 | 50 | gini | 0.832298 | 0.901802 |
| 664 | 0.200000 | 5 | 50 | entropy | 0.826087 | 0.900559 |
| 665 | 0.200000 | 5 | 50 | log_loss | 0.813665 | 0.885643 |
| 666 | 0.200000 | 5 | 100 | gini | 0.875776 | 0.916718 |
| 667 | 0.200000 | 5 | 100 | entropy | 0.832298 | 0.896209 |
| 668 | 0.200000 | 5 | 100 | log_loss | 0.838509 | 0.904288 |
| 669 | 0.200000 | 5 | 200 | gini | 0.857143 | 0.911125 |
| 670 | 0.200000 | 5 | 200 | entropy | 0.835404 | 0.899938 |
| 671 | 0.200000 | 5 | 200 | log_loss | 0.841615 | 0.897452 |
| 672 | 0.200000 | 5 | 300 | gini | 0.835404 | 0.914232 |
| 673 | 0.200000 | 5 | 300 | entropy | 0.829193 | 0.896830 |
| 674 | 0.200000 | 5 | 300 | log_loss | 0.832298 | 0.894966 |
| 675 | 0.200000 | 6 | 10 | gini | 0.757764 | 0.865755 |
| 676 | 0.200000 | 6 | 10 | entropy | 0.791925 | 0.881293 |
| 677 | 0.200000 | 6 | 10 | log_loss | 0.804348 | 0.894966 |
| 678 | 0.200000 | 6 | 50 | gini | 0.844720 | 0.925420 |
| 679 | 0.200000 | 6 | 50 | entropy | 0.847826 | 0.931013 |
| 680 | 0.200000 | 6 | 50 | log_loss | 0.829193 | 0.926663 |
| 681 | 0.200000 | 6 | 100 | gini | 0.854037 | 0.937228 |
| 682 | 0.200000 | 6 | 100 | entropy | 0.854037 | 0.932256 |
| 683 | 0.200000 | 6 | 100 | log_loss | 0.850932 | 0.934121 |
| 684 | 0.200000 | 6 | 200 | gini | 0.860248 | 0.940957 |
| 685 | 0.200000 | 6 | 200 | entropy | 0.841615 | 0.929770 |
| 686 | 0.200000 | 6 | 200 | log_loss | 0.844720 | 0.938471 |
| 687 | 0.200000 | 6 | 300 | gini | 0.847826 | 0.936607 |
| 688 | 0.200000 | 6 | 300 | entropy | 0.847826 | 0.934121 |
| 689 | 0.200000 | 6 | 300 | log_loss | 0.841615 | 0.931635 |
| 690 | 0.200000 | 7 | 10 | gini | 0.826087 | 0.918583 |
| 691 | 0.200000 | 7 | 10 | entropy | 0.785714 | 0.904910 |
| 692 | 0.200000 | 7 | 10 | log_loss | 0.860248 | 0.934742 |
| 693 | 0.200000 | 7 | 50 | gini | 0.860248 | 0.944065 |
| 694 | 0.200000 | 7 | 50 | entropy | 0.881988 | 0.958359 |
| 695 | 0.200000 | 7 | 50 | log_loss | 0.844720 | 0.954009 |
| 696 | 0.200000 | 7 | 100 | gini | 0.860248 | 0.956495 |
| 697 | 0.200000 | 7 | 100 | entropy | 0.869565 | 0.960845 |
| 698 | 0.200000 | 7 | 100 | log_loss | 0.854037 | 0.954630 |
| 699 | 0.200000 | 7 | 200 | gini | 0.863354 | 0.959602 |
| 700 | 0.200000 | 7 | 200 | entropy | 0.857143 | 0.956495 |
| 701 | 0.200000 | 7 | 200 | log_loss | 0.860248 | 0.957116 |
| 702 | 0.200000 | 7 | 300 | gini | 0.860248 | 0.954009 |
| 703 | 0.200000 | 7 | 300 | entropy | 0.872671 | 0.963953 |
| 704 | 0.200000 | 7 | 300 | log_loss | 0.850932 | 0.956495 |
| 705 | 0.200000 | 8 | 10 | gini | 0.813665 | 0.933499 |
| 706 | 0.200000 | 8 | 10 | entropy | 0.816770 | 0.937850 |
| 707 | 0.200000 | 8 | 10 | log_loss | 0.813665 | 0.941579 |
| 708 | 0.200000 | 8 | 50 | gini | 0.885093 | 0.970789 |
| 709 | 0.200000 | 8 | 50 | entropy | 0.875776 | 0.969546 |
| 710 | 0.200000 | 8 | 50 | log_loss | 0.844720 | 0.962088 |
| 711 | 0.200000 | 8 | 100 | gini | 0.875776 | 0.968303 |
| 712 | 0.200000 | 8 | 100 | entropy | 0.863354 | 0.969546 |
| 713 | 0.200000 | 8 | 100 | log_loss | 0.869565 | 0.969546 |
| 714 | 0.200000 | 8 | 200 | gini | 0.888199 | 0.970789 |
| 715 | 0.200000 | 8 | 200 | entropy | 0.860248 | 0.968303 |
| 716 | 0.200000 | 8 | 200 | log_loss | 0.878882 | 0.973275 |
| 717 | 0.200000 | 8 | 300 | gini | 0.872671 | 0.968303 |
| 718 | 0.200000 | 8 | 300 | entropy | 0.869565 | 0.968925 |
| 719 | 0.200000 | 8 | 300 | log_loss | 0.878882 | 0.972654 |
| 720 | 0.200000 | 9 | 10 | gini | 0.798137 | 0.940336 |
| 721 | 0.200000 | 9 | 10 | entropy | 0.826087 | 0.953387 |
| 722 | 0.200000 | 9 | 10 | log_loss | 0.826087 | 0.949658 |
| 723 | 0.200000 | 9 | 50 | gini | 0.869565 | 0.972654 |
| 724 | 0.200000 | 9 | 50 | entropy | 0.860248 | 0.972032 |
| 725 | 0.200000 | 9 | 50 | log_loss | 0.881988 | 0.975140 |
| 726 | 0.200000 | 9 | 100 | gini | 0.866460 | 0.970789 |
| 727 | 0.200000 | 9 | 100 | entropy | 0.869565 | 0.973275 |
| 728 | 0.200000 | 9 | 100 | log_loss | 0.869565 | 0.973275 |
| 729 | 0.200000 | 9 | 200 | gini | 0.875776 | 0.973897 |
| 730 | 0.200000 | 9 | 200 | entropy | 0.866460 | 0.972654 |
| 731 | 0.200000 | 9 | 200 | log_loss | 0.872671 | 0.974518 |
| 732 | 0.200000 | 9 | 300 | gini | 0.878882 | 0.973897 |
| 733 | 0.200000 | 9 | 300 | entropy | 0.872671 | 0.974518 |
| 734 | 0.200000 | 9 | 300 | log_loss | 0.872671 | 0.973897 |
| 735 | 0.200000 | 10 | 10 | gini | 0.838509 | 0.953387 |
| 736 | 0.200000 | 10 | 10 | entropy | 0.819876 | 0.958981 |
| 737 | 0.200000 | 10 | 10 | log_loss | 0.807453 | 0.955252 |
| 738 | 0.200000 | 10 | 50 | gini | 0.878882 | 0.973897 |
| 739 | 0.200000 | 10 | 50 | entropy | 0.863354 | 0.972032 |
| 740 | 0.200000 | 10 | 50 | log_loss | 0.878882 | 0.975140 |
| 741 | 0.200000 | 10 | 100 | gini | 0.888199 | 0.977626 |
| 742 | 0.200000 | 10 | 100 | entropy | 0.885093 | 0.977004 |
| 743 | 0.200000 | 10 | 100 | log_loss | 0.881988 | 0.976383 |
| 744 | 0.200000 | 10 | 200 | gini | 0.875776 | 0.974518 |
| 745 | 0.200000 | 10 | 200 | entropy | 0.872671 | 0.974518 |
| 746 | 0.200000 | 10 | 200 | log_loss | 0.872671 | 0.974518 |
| 747 | 0.200000 | 10 | 300 | gini | 0.878882 | 0.975761 |
| 748 | 0.200000 | 10 | 300 | entropy | 0.875776 | 0.975140 |
| 749 | 0.200000 | 10 | 300 | log_loss | 0.891304 | 0.978247 |
| 750 | 0.200000 | 11 | 10 | gini | 0.804348 | 0.954009 |
| 751 | 0.200000 | 11 | 10 | entropy | 0.835404 | 0.961467 |
| 752 | 0.200000 | 11 | 10 | log_loss | 0.819876 | 0.961467 |
| 753 | 0.200000 | 11 | 50 | gini | 0.844720 | 0.967682 |
| 754 | 0.200000 | 11 | 50 | entropy | 0.875776 | 0.974518 |
| 755 | 0.200000 | 11 | 50 | log_loss | 0.863354 | 0.972654 |
| 756 | 0.200000 | 11 | 100 | gini | 0.869565 | 0.973897 |
| 757 | 0.200000 | 11 | 100 | entropy | 0.888199 | 0.977626 |
| 758 | 0.200000 | 11 | 100 | log_loss | 0.872671 | 0.974518 |
| 759 | 0.200000 | 11 | 200 | gini | 0.872671 | 0.974518 |
| 760 | 0.200000 | 11 | 200 | entropy | 0.894410 | 0.978869 |
| 761 | 0.200000 | 11 | 200 | log_loss | 0.881988 | 0.976383 |
| 762 | 0.200000 | 11 | 300 | gini | 0.881988 | 0.976383 |
| 763 | 0.200000 | 11 | 300 | entropy | 0.878882 | 0.975761 |
| 764 | 0.200000 | 11 | 300 | log_loss | 0.897516 | 0.979490 |
| 765 | 0.200000 | 12 | 10 | gini | 0.788820 | 0.954630 |
| 766 | 0.200000 | 12 | 10 | entropy | 0.785714 | 0.954630 |
| 767 | 0.200000 | 12 | 10 | log_loss | 0.788820 | 0.951523 |
| 768 | 0.200000 | 12 | 50 | gini | 0.878882 | 0.975761 |
| 769 | 0.200000 | 12 | 50 | entropy | 0.869565 | 0.973897 |
| 770 | 0.200000 | 12 | 50 | log_loss | 0.863354 | 0.972654 |
| 771 | 0.200000 | 12 | 100 | gini | 0.888199 | 0.977626 |
| 772 | 0.200000 | 12 | 100 | entropy | 0.885093 | 0.977004 |
| 773 | 0.200000 | 12 | 100 | log_loss | 0.869565 | 0.973897 |
| 774 | 0.200000 | 12 | 200 | gini | 0.891304 | 0.978247 |
| 775 | 0.200000 | 12 | 200 | entropy | 0.891304 | 0.978247 |
| 776 | 0.200000 | 12 | 200 | log_loss | 0.878882 | 0.975761 |
| 777 | 0.200000 | 12 | 300 | gini | 0.900621 | 0.980112 |
| 778 | 0.200000 | 12 | 300 | entropy | 0.888199 | 0.977626 |
| 779 | 0.200000 | 12 | 300 | log_loss | 0.888199 | 0.977626 |
| 780 | 0.200000 | 13 | 10 | gini | 0.801242 | 0.954630 |
| 781 | 0.200000 | 13 | 10 | entropy | 0.816770 | 0.961467 |
| 782 | 0.200000 | 13 | 10 | log_loss | 0.844720 | 0.965817 |
| 783 | 0.200000 | 13 | 50 | gini | 0.881988 | 0.976383 |
| 784 | 0.200000 | 13 | 50 | entropy | 0.875776 | 0.975140 |
| 785 | 0.200000 | 13 | 50 | log_loss | 0.860248 | 0.972032 |
| 786 | 0.200000 | 13 | 100 | gini | 0.881988 | 0.976383 |
| 787 | 0.200000 | 13 | 100 | entropy | 0.866460 | 0.973275 |
| 788 | 0.200000 | 13 | 100 | log_loss | 0.878882 | 0.975761 |
| 789 | 0.200000 | 13 | 200 | gini | 0.888199 | 0.977626 |
| 790 | 0.200000 | 13 | 200 | entropy | 0.885093 | 0.977004 |
| 791 | 0.200000 | 13 | 200 | log_loss | 0.881988 | 0.976383 |
| 792 | 0.200000 | 13 | 300 | gini | 0.881988 | 0.976383 |
| 793 | 0.200000 | 13 | 300 | entropy | 0.888199 | 0.977626 |
| 794 | 0.200000 | 13 | 300 | log_loss | 0.872671 | 0.974518 |
| 795 | 0.200000 | 14 | 10 | gini | 0.751553 | 0.947794 |
| 796 | 0.200000 | 14 | 10 | entropy | 0.844720 | 0.962088 |
| 797 | 0.200000 | 14 | 10 | log_loss | 0.776398 | 0.952766 |
| 798 | 0.200000 | 14 | 50 | gini | 0.854037 | 0.970789 |
| 799 | 0.200000 | 14 | 50 | entropy | 0.881988 | 0.976383 |
| 800 | 0.200000 | 14 | 50 | log_loss | 0.844720 | 0.968925 |
| 801 | 0.200000 | 14 | 100 | gini | 0.875776 | 0.975140 |
| 802 | 0.200000 | 14 | 100 | entropy | 0.894410 | 0.978869 |
| 803 | 0.200000 | 14 | 100 | log_loss | 0.875776 | 0.975140 |
| 804 | 0.200000 | 14 | 200 | gini | 0.885093 | 0.977004 |
| 805 | 0.200000 | 14 | 200 | entropy | 0.881988 | 0.976383 |
| 806 | 0.200000 | 14 | 200 | log_loss | 0.878882 | 0.975761 |
| 807 | 0.200000 | 14 | 300 | gini | 0.888199 | 0.977626 |
| 808 | 0.200000 | 14 | 300 | entropy | 0.891304 | 0.978247 |
| 809 | 0.200000 | 14 | 300 | log_loss | 0.885093 | 0.977004 |
| 810 | 0.200000 | 15 | 10 | gini | 0.804348 | 0.955252 |
| 811 | 0.200000 | 15 | 10 | entropy | 0.795031 | 0.956495 |
| 812 | 0.200000 | 15 | 10 | log_loss | 0.779503 | 0.952766 |
| 813 | 0.200000 | 15 | 50 | gini | 0.875776 | 0.975140 |
| 814 | 0.200000 | 15 | 50 | entropy | 0.854037 | 0.970789 |
| 815 | 0.200000 | 15 | 50 | log_loss | 0.844720 | 0.968925 |
| 816 | 0.200000 | 15 | 100 | gini | 0.885093 | 0.977004 |
| 817 | 0.200000 | 15 | 100 | entropy | 0.888199 | 0.977626 |
| 818 | 0.200000 | 15 | 100 | log_loss | 0.875776 | 0.975140 |
| 819 | 0.200000 | 15 | 200 | gini | 0.881988 | 0.976383 |
| 820 | 0.200000 | 15 | 200 | entropy | 0.878882 | 0.975761 |
| 821 | 0.200000 | 15 | 200 | log_loss | 0.878882 | 0.975761 |
| 822 | 0.200000 | 15 | 300 | gini | 0.894410 | 0.978869 |
| 823 | 0.200000 | 15 | 300 | entropy | 0.891304 | 0.978247 |
| 824 | 0.200000 | 15 | 300 | log_loss | 0.888199 | 0.977626 |
| 825 | 0.200000 | 16 | 10 | gini | 0.847826 | 0.964574 |
| 826 | 0.200000 | 16 | 10 | entropy | 0.807453 | 0.956495 |
| 827 | 0.200000 | 16 | 10 | log_loss | 0.788820 | 0.953387 |
| 828 | 0.200000 | 16 | 50 | gini | 0.857143 | 0.971411 |
| 829 | 0.200000 | 16 | 50 | entropy | 0.869565 | 0.973897 |
| 830 | 0.200000 | 16 | 50 | log_loss | 0.891304 | 0.978247 |
| 831 | 0.200000 | 16 | 100 | gini | 0.894410 | 0.978869 |
| 832 | 0.200000 | 16 | 100 | entropy | 0.875776 | 0.975140 |
| 833 | 0.200000 | 16 | 100 | log_loss | 0.869565 | 0.973897 |
| 834 | 0.200000 | 16 | 200 | gini | 0.888199 | 0.977626 |
| 835 | 0.200000 | 16 | 200 | entropy | 0.885093 | 0.977004 |
| 836 | 0.200000 | 16 | 200 | log_loss | 0.875776 | 0.975140 |
| 837 | 0.200000 | 16 | 300 | gini | 0.885093 | 0.977004 |
| 838 | 0.200000 | 16 | 300 | entropy | 0.885093 | 0.977004 |
| 839 | 0.200000 | 16 | 300 | log_loss | 0.878882 | 0.975761 |
| 840 | 0.200000 | 17 | 10 | gini | 0.807453 | 0.960224 |
| 841 | 0.200000 | 17 | 10 | entropy | 0.826087 | 0.961467 |
| 842 | 0.200000 | 17 | 10 | log_loss | 0.810559 | 0.955873 |
| 843 | 0.200000 | 17 | 50 | gini | 0.863354 | 0.972654 |
| 844 | 0.200000 | 17 | 50 | entropy | 0.881988 | 0.976383 |
| 845 | 0.200000 | 17 | 50 | log_loss | 0.863354 | 0.972654 |
| 846 | 0.200000 | 17 | 100 | gini | 0.897516 | 0.979490 |
| 847 | 0.200000 | 17 | 100 | entropy | 0.900621 | 0.980112 |
| 848 | 0.200000 | 17 | 100 | log_loss | 0.860248 | 0.972032 |
| 849 | 0.200000 | 17 | 200 | gini | 0.878882 | 0.975761 |
| 850 | 0.200000 | 17 | 200 | entropy | 0.897516 | 0.979490 |
| 851 | 0.200000 | 17 | 200 | log_loss | 0.872671 | 0.974518 |
| 852 | 0.200000 | 17 | 300 | gini | 0.888199 | 0.977626 |
| 853 | 0.200000 | 17 | 300 | entropy | 0.885093 | 0.977004 |
| 854 | 0.200000 | 17 | 300 | log_loss | 0.881988 | 0.976383 |
| 855 | 0.200000 | 18 | 10 | gini | 0.739130 | 0.941579 |
| 856 | 0.200000 | 18 | 10 | entropy | 0.816770 | 0.961467 |
| 857 | 0.200000 | 18 | 10 | log_loss | 0.785714 | 0.952766 |
| 858 | 0.200000 | 18 | 50 | gini | 0.875776 | 0.975140 |
| 859 | 0.200000 | 18 | 50 | entropy | 0.863354 | 0.972654 |
| 860 | 0.200000 | 18 | 50 | log_loss | 0.872671 | 0.974518 |
| 861 | 0.200000 | 18 | 100 | gini | 0.885093 | 0.977004 |
| 862 | 0.200000 | 18 | 100 | entropy | 0.881988 | 0.976383 |
| 863 | 0.200000 | 18 | 100 | log_loss | 0.872671 | 0.974518 |
| 864 | 0.200000 | 18 | 200 | gini | 0.891304 | 0.978247 |
| 865 | 0.200000 | 18 | 200 | entropy | 0.866460 | 0.973275 |
| 866 | 0.200000 | 18 | 200 | log_loss | 0.885093 | 0.977004 |
| 867 | 0.200000 | 18 | 300 | gini | 0.881988 | 0.976383 |
| 868 | 0.200000 | 18 | 300 | entropy | 0.885093 | 0.977004 |
| 869 | 0.200000 | 18 | 300 | log_loss | 0.891304 | 0.978247 |
| 870 | 0.200000 | 19 | 10 | gini | 0.804348 | 0.960224 |
| 871 | 0.200000 | 19 | 10 | entropy | 0.819876 | 0.960845 |
| 872 | 0.200000 | 19 | 10 | log_loss | 0.841615 | 0.962710 |
| 873 | 0.200000 | 19 | 50 | gini | 0.857143 | 0.971411 |
| 874 | 0.200000 | 19 | 50 | entropy | 0.860248 | 0.972032 |
| 875 | 0.200000 | 19 | 50 | log_loss | 0.869565 | 0.973897 |
| 876 | 0.200000 | 19 | 100 | gini | 0.878882 | 0.975761 |
| 877 | 0.200000 | 19 | 100 | entropy | 0.866460 | 0.973275 |
| 878 | 0.200000 | 19 | 100 | log_loss | 0.894410 | 0.978869 |
| 879 | 0.200000 | 19 | 200 | gini | 0.888199 | 0.977626 |
| 880 | 0.200000 | 19 | 200 | entropy | 0.888199 | 0.977626 |
| 881 | 0.200000 | 19 | 200 | log_loss | 0.878882 | 0.975761 |
| 882 | 0.200000 | 19 | 300 | gini | 0.885093 | 0.977004 |
| 883 | 0.200000 | 19 | 300 | entropy | 0.888199 | 0.977626 |
| 884 | 0.200000 | 19 | 300 | log_loss | 0.881988 | 0.976383 |
| 885 | 0.200000 | 20 | 10 | gini | 0.810559 | 0.958981 |
| 886 | 0.200000 | 20 | 10 | entropy | 0.826087 | 0.959602 |
| 887 | 0.200000 | 20 | 10 | log_loss | 0.810559 | 0.957738 |
| 888 | 0.200000 | 20 | 50 | gini | 0.878882 | 0.975761 |
| 889 | 0.200000 | 20 | 50 | entropy | 0.869565 | 0.973897 |
| 890 | 0.200000 | 20 | 50 | log_loss | 0.885093 | 0.977004 |
| 891 | 0.200000 | 20 | 100 | gini | 0.872671 | 0.974518 |
| 892 | 0.200000 | 20 | 100 | entropy | 0.888199 | 0.977626 |
| 893 | 0.200000 | 20 | 100 | log_loss | 0.869565 | 0.973897 |
| 894 | 0.200000 | 20 | 200 | gini | 0.885093 | 0.977004 |
| 895 | 0.200000 | 20 | 200 | entropy | 0.885093 | 0.977004 |
| 896 | 0.200000 | 20 | 200 | log_loss | 0.872671 | 0.974518 |
| 897 | 0.200000 | 20 | 300 | gini | 0.878882 | 0.975761 |
| 898 | 0.200000 | 20 | 300 | entropy | 0.878882 | 0.975761 |
| 899 | 0.200000 | 20 | 300 | log_loss | 0.885093 | 0.977004 |
| 900 | 0.250000 | 1 | 10 | gini | 0.523573 | 0.539466 |
| 901 | 0.250000 | 1 | 10 | entropy | 0.543424 | 0.527035 |
| 902 | 0.250000 | 1 | 10 | log_loss | 0.553350 | 0.554382 |
| 903 | 0.250000 | 1 | 50 | gini | 0.660050 | 0.685519 |
| 904 | 0.250000 | 1 | 50 | entropy | 0.550868 | 0.574891 |
| 905 | 0.250000 | 1 | 50 | log_loss | 0.585608 | 0.582349 |
| 906 | 0.250000 | 1 | 100 | gini | 0.657568 | 0.691734 |
| 907 | 0.250000 | 1 | 100 | entropy | 0.570720 | 0.567433 |
| 908 | 0.250000 | 1 | 100 | log_loss | 0.570720 | 0.589186 |
| 909 | 0.250000 | 1 | 200 | gini | 0.662531 | 0.697328 |
| 910 | 0.250000 | 1 | 200 | entropy | 0.590571 | 0.580485 |
| 911 | 0.250000 | 1 | 200 | log_loss | 0.565757 | 0.569298 |
| 912 | 0.250000 | 1 | 300 | gini | 0.669975 | 0.701057 |
| 913 | 0.250000 | 1 | 300 | entropy | 0.583127 | 0.587321 |
| 914 | 0.250000 | 1 | 300 | log_loss | 0.593052 | 0.600994 |
| 915 | 0.250000 | 2 | 10 | gini | 0.754342 | 0.765693 |
| 916 | 0.250000 | 2 | 10 | entropy | 0.682382 | 0.700435 |
| 917 | 0.250000 | 2 | 10 | log_loss | 0.660050 | 0.704164 |
| 918 | 0.250000 | 2 | 50 | gini | 0.722084 | 0.752641 |
| 919 | 0.250000 | 2 | 50 | entropy | 0.632754 | 0.645121 |
| 920 | 0.250000 | 2 | 50 | log_loss | 0.689826 | 0.717216 |
| 921 | 0.250000 | 2 | 100 | gini | 0.774194 | 0.799254 |
| 922 | 0.250000 | 2 | 100 | entropy | 0.724566 | 0.742076 |
| 923 | 0.250000 | 2 | 100 | log_loss | 0.774194 | 0.789932 |
| 924 | 0.250000 | 2 | 200 | gini | 0.764268 | 0.791796 |
| 925 | 0.250000 | 2 | 200 | entropy | 0.744417 | 0.766314 |
| 926 | 0.250000 | 2 | 200 | log_loss | 0.727047 | 0.753263 |
| 927 | 0.250000 | 2 | 300 | gini | 0.761787 | 0.793039 |
| 928 | 0.250000 | 2 | 300 | entropy | 0.769231 | 0.782474 |
| 929 | 0.250000 | 2 | 300 | log_loss | 0.704715 | 0.720945 |
| 930 | 0.250000 | 3 | 10 | gini | 0.518610 | 0.580485 |
| 931 | 0.250000 | 3 | 10 | entropy | 0.734491 | 0.747048 |
| 932 | 0.250000 | 3 | 10 | log_loss | 0.642680 | 0.677439 |
| 933 | 0.250000 | 3 | 50 | gini | 0.781638 | 0.817899 |
| 934 | 0.250000 | 3 | 50 | entropy | 0.729529 | 0.782474 |
| 935 | 0.250000 | 3 | 50 | log_loss | 0.796526 | 0.836544 |
| 936 | 0.250000 | 3 | 100 | gini | 0.808933 | 0.839030 |
| 937 | 0.250000 | 3 | 100 | entropy | 0.781638 | 0.817278 |
| 938 | 0.250000 | 3 | 100 | log_loss | 0.806452 | 0.839652 |
| 939 | 0.250000 | 3 | 200 | gini | 0.794045 | 0.833437 |
| 940 | 0.250000 | 3 | 200 | entropy | 0.801489 | 0.835301 |
| 941 | 0.250000 | 3 | 200 | log_loss | 0.826303 | 0.841516 |
| 942 | 0.250000 | 3 | 300 | gini | 0.808933 | 0.845867 |
| 943 | 0.250000 | 3 | 300 | entropy | 0.808933 | 0.836544 |
| 944 | 0.250000 | 3 | 300 | log_loss | 0.813896 | 0.845867 |
| 945 | 0.250000 | 4 | 10 | gini | 0.771712 | 0.803605 |
| 946 | 0.250000 | 4 | 10 | entropy | 0.674938 | 0.747669 |
| 947 | 0.250000 | 4 | 10 | log_loss | 0.756824 | 0.810441 |
| 948 | 0.250000 | 4 | 50 | gini | 0.776675 | 0.847110 |
| 949 | 0.250000 | 4 | 50 | entropy | 0.806452 | 0.858297 |
| 950 | 0.250000 | 4 | 50 | log_loss | 0.799007 | 0.856433 |
| 951 | 0.250000 | 4 | 100 | gini | 0.816377 | 0.875078 |
| 952 | 0.250000 | 4 | 100 | entropy | 0.836228 | 0.875078 |
| 953 | 0.250000 | 4 | 100 | log_loss | 0.808933 | 0.867620 |
| 954 | 0.250000 | 4 | 200 | gini | 0.828784 | 0.877564 |
| 955 | 0.250000 | 4 | 200 | entropy | 0.823821 | 0.869484 |
| 956 | 0.250000 | 4 | 200 | log_loss | 0.813896 | 0.868863 |
| 957 | 0.250000 | 4 | 300 | gini | 0.828784 | 0.871349 |
| 958 | 0.250000 | 4 | 300 | entropy | 0.826303 | 0.875699 |
| 959 | 0.250000 | 4 | 300 | log_loss | 0.826303 | 0.877564 |
| 960 | 0.250000 | 5 | 10 | gini | 0.744417 | 0.837166 |
| 961 | 0.250000 | 5 | 10 | entropy | 0.769231 | 0.845245 |
| 962 | 0.250000 | 5 | 10 | log_loss | 0.789082 | 0.844624 |
| 963 | 0.250000 | 5 | 50 | gini | 0.836228 | 0.904910 |
| 964 | 0.250000 | 5 | 50 | entropy | 0.811414 | 0.891858 |
| 965 | 0.250000 | 5 | 50 | log_loss | 0.833747 | 0.893101 |
| 966 | 0.250000 | 5 | 100 | gini | 0.843672 | 0.901181 |
| 967 | 0.250000 | 5 | 100 | entropy | 0.826303 | 0.891237 |
| 968 | 0.250000 | 5 | 100 | log_loss | 0.823821 | 0.896830 |
| 969 | 0.250000 | 5 | 200 | gini | 0.833747 | 0.902424 |
| 970 | 0.250000 | 5 | 200 | entropy | 0.833747 | 0.898695 |
| 971 | 0.250000 | 5 | 200 | log_loss | 0.838710 | 0.894344 |
| 972 | 0.250000 | 5 | 300 | gini | 0.831266 | 0.904288 |
| 973 | 0.250000 | 5 | 300 | entropy | 0.838710 | 0.898073 |
| 974 | 0.250000 | 5 | 300 | log_loss | 0.846154 | 0.901802 |
| 975 | 0.250000 | 6 | 10 | gini | 0.769231 | 0.870727 |
| 976 | 0.250000 | 6 | 10 | entropy | 0.781638 | 0.898695 |
| 977 | 0.250000 | 6 | 10 | log_loss | 0.794045 | 0.881914 |
| 978 | 0.250000 | 6 | 50 | gini | 0.836228 | 0.920447 |
| 979 | 0.250000 | 6 | 50 | entropy | 0.841191 | 0.923555 |
| 980 | 0.250000 | 6 | 50 | log_loss | 0.848635 | 0.926663 |
| 981 | 0.250000 | 6 | 100 | gini | 0.833747 | 0.923555 |
| 982 | 0.250000 | 6 | 100 | entropy | 0.851117 | 0.927284 |
| 983 | 0.250000 | 6 | 100 | log_loss | 0.841191 | 0.927906 |
| 984 | 0.250000 | 6 | 200 | gini | 0.851117 | 0.932878 |
| 985 | 0.250000 | 6 | 200 | entropy | 0.843672 | 0.926663 |
| 986 | 0.250000 | 6 | 200 | log_loss | 0.858561 | 0.929770 |
| 987 | 0.250000 | 6 | 300 | gini | 0.863524 | 0.936607 |
| 988 | 0.250000 | 6 | 300 | entropy | 0.848635 | 0.930392 |
| 989 | 0.250000 | 6 | 300 | log_loss | 0.853598 | 0.931013 |
| 990 | 0.250000 | 7 | 10 | gini | 0.781638 | 0.911746 |
| 991 | 0.250000 | 7 | 10 | entropy | 0.826303 | 0.925420 |
| 992 | 0.250000 | 7 | 10 | log_loss | 0.741935 | 0.877564 |
| 993 | 0.250000 | 7 | 50 | gini | 0.851117 | 0.940336 |
| 994 | 0.250000 | 7 | 50 | entropy | 0.833747 | 0.940957 |
| 995 | 0.250000 | 7 | 50 | log_loss | 0.858561 | 0.951523 |
| 996 | 0.250000 | 7 | 100 | gini | 0.861042 | 0.950280 |
| 997 | 0.250000 | 7 | 100 | entropy | 0.841191 | 0.942822 |
| 998 | 0.250000 | 7 | 100 | log_loss | 0.873449 | 0.952766 |
| 999 | 0.250000 | 7 | 200 | gini | 0.853598 | 0.949658 |
| 1000 | 0.250000 | 7 | 200 | entropy | 0.858561 | 0.954630 |
| 1001 | 0.250000 | 7 | 200 | log_loss | 0.866005 | 0.955873 |
| 1002 | 0.250000 | 7 | 300 | gini | 0.863524 | 0.950280 |
| 1003 | 0.250000 | 7 | 300 | entropy | 0.856079 | 0.952144 |
| 1004 | 0.250000 | 7 | 300 | log_loss | 0.863524 | 0.954630 |
| 1005 | 0.250000 | 8 | 10 | gini | 0.803970 | 0.922312 |
| 1006 | 0.250000 | 8 | 10 | entropy | 0.796526 | 0.924798 |
| 1007 | 0.250000 | 8 | 10 | log_loss | 0.843672 | 0.946551 |
| 1008 | 0.250000 | 8 | 50 | gini | 0.843672 | 0.953387 |
| 1009 | 0.250000 | 8 | 50 | entropy | 0.863524 | 0.960224 |
| 1010 | 0.250000 | 8 | 50 | log_loss | 0.836228 | 0.949658 |
| 1011 | 0.250000 | 8 | 100 | gini | 0.866005 | 0.960845 |
| 1012 | 0.250000 | 8 | 100 | entropy | 0.863524 | 0.961467 |
| 1013 | 0.250000 | 8 | 100 | log_loss | 0.880893 | 0.966439 |
| 1014 | 0.250000 | 8 | 200 | gini | 0.858561 | 0.957738 |
| 1015 | 0.250000 | 8 | 200 | entropy | 0.870968 | 0.966439 |
| 1016 | 0.250000 | 8 | 200 | log_loss | 0.870968 | 0.965817 |
| 1017 | 0.250000 | 8 | 300 | gini | 0.851117 | 0.958359 |
| 1018 | 0.250000 | 8 | 300 | entropy | 0.856079 | 0.961467 |
| 1019 | 0.250000 | 8 | 300 | log_loss | 0.873449 | 0.964574 |
| 1020 | 0.250000 | 9 | 10 | gini | 0.769231 | 0.923555 |
| 1021 | 0.250000 | 9 | 10 | entropy | 0.789082 | 0.937228 |
| 1022 | 0.250000 | 9 | 10 | log_loss | 0.784119 | 0.926663 |
| 1023 | 0.250000 | 9 | 50 | gini | 0.828784 | 0.954630 |
| 1024 | 0.250000 | 9 | 50 | entropy | 0.858561 | 0.961467 |
| 1025 | 0.250000 | 9 | 50 | log_loss | 0.851117 | 0.961467 |
| 1026 | 0.250000 | 9 | 100 | gini | 0.856079 | 0.962088 |
| 1027 | 0.250000 | 9 | 100 | entropy | 0.866005 | 0.966439 |
| 1028 | 0.250000 | 9 | 100 | log_loss | 0.848635 | 0.962088 |
| 1029 | 0.250000 | 9 | 200 | gini | 0.875931 | 0.967682 |
| 1030 | 0.250000 | 9 | 200 | entropy | 0.870968 | 0.967060 |
| 1031 | 0.250000 | 9 | 200 | log_loss | 0.873449 | 0.967682 |
| 1032 | 0.250000 | 9 | 300 | gini | 0.873449 | 0.966439 |
| 1033 | 0.250000 | 9 | 300 | entropy | 0.875931 | 0.968925 |
| 1034 | 0.250000 | 9 | 300 | log_loss | 0.875931 | 0.968303 |
| 1035 | 0.250000 | 10 | 10 | gini | 0.796526 | 0.939714 |
| 1036 | 0.250000 | 10 | 10 | entropy | 0.803970 | 0.942822 |
| 1037 | 0.250000 | 10 | 10 | log_loss | 0.789082 | 0.943443 |
| 1038 | 0.250000 | 10 | 50 | gini | 0.848635 | 0.960845 |
| 1039 | 0.250000 | 10 | 50 | entropy | 0.866005 | 0.966439 |
| 1040 | 0.250000 | 10 | 50 | log_loss | 0.863524 | 0.965817 |
| 1041 | 0.250000 | 10 | 100 | gini | 0.873449 | 0.967682 |
| 1042 | 0.250000 | 10 | 100 | entropy | 0.861042 | 0.965196 |
| 1043 | 0.250000 | 10 | 100 | log_loss | 0.873449 | 0.968303 |
| 1044 | 0.250000 | 10 | 200 | gini | 0.878412 | 0.968925 |
| 1045 | 0.250000 | 10 | 200 | entropy | 0.880893 | 0.970168 |
| 1046 | 0.250000 | 10 | 200 | log_loss | 0.873449 | 0.968303 |
| 1047 | 0.250000 | 10 | 300 | gini | 0.866005 | 0.965817 |
| 1048 | 0.250000 | 10 | 300 | entropy | 0.880893 | 0.970168 |
| 1049 | 0.250000 | 10 | 300 | log_loss | 0.863524 | 0.965817 |
| 1050 | 0.250000 | 11 | 10 | gini | 0.789082 | 0.939093 |
| 1051 | 0.250000 | 11 | 10 | entropy | 0.761787 | 0.934742 |
| 1052 | 0.250000 | 11 | 10 | log_loss | 0.776675 | 0.937228 |
| 1053 | 0.250000 | 11 | 50 | gini | 0.866005 | 0.966439 |
| 1054 | 0.250000 | 11 | 50 | entropy | 0.880893 | 0.970168 |
| 1055 | 0.250000 | 11 | 50 | log_loss | 0.863524 | 0.965817 |
| 1056 | 0.250000 | 11 | 100 | gini | 0.856079 | 0.963331 |
| 1057 | 0.250000 | 11 | 100 | entropy | 0.866005 | 0.966439 |
| 1058 | 0.250000 | 11 | 100 | log_loss | 0.880893 | 0.970168 |
| 1059 | 0.250000 | 11 | 200 | gini | 0.878412 | 0.969546 |
| 1060 | 0.250000 | 11 | 200 | entropy | 0.895782 | 0.973897 |
| 1061 | 0.250000 | 11 | 200 | log_loss | 0.880893 | 0.970168 |
| 1062 | 0.250000 | 11 | 300 | gini | 0.875931 | 0.968925 |
| 1063 | 0.250000 | 11 | 300 | entropy | 0.868486 | 0.967060 |
| 1064 | 0.250000 | 11 | 300 | log_loss | 0.858561 | 0.964574 |
| 1065 | 0.250000 | 12 | 10 | gini | 0.796526 | 0.941579 |
| 1066 | 0.250000 | 12 | 10 | entropy | 0.818859 | 0.952144 |
| 1067 | 0.250000 | 12 | 10 | log_loss | 0.816377 | 0.949658 |
| 1068 | 0.250000 | 12 | 50 | gini | 0.868486 | 0.967060 |
| 1069 | 0.250000 | 12 | 50 | entropy | 0.870968 | 0.967682 |
| 1070 | 0.250000 | 12 | 50 | log_loss | 0.858561 | 0.964574 |
| 1071 | 0.250000 | 12 | 100 | gini | 0.866005 | 0.966439 |
| 1072 | 0.250000 | 12 | 100 | entropy | 0.875931 | 0.968925 |
| 1073 | 0.250000 | 12 | 100 | log_loss | 0.895782 | 0.973897 |
| 1074 | 0.250000 | 12 | 200 | gini | 0.873449 | 0.968303 |
| 1075 | 0.250000 | 12 | 200 | entropy | 0.883375 | 0.970789 |
| 1076 | 0.250000 | 12 | 200 | log_loss | 0.885856 | 0.971411 |
| 1077 | 0.250000 | 12 | 300 | gini | 0.885856 | 0.971411 |
| 1078 | 0.250000 | 12 | 300 | entropy | 0.870968 | 0.967682 |
| 1079 | 0.250000 | 12 | 300 | log_loss | 0.870968 | 0.967682 |
| 1080 | 0.250000 | 13 | 10 | gini | 0.833747 | 0.952766 |
| 1081 | 0.250000 | 13 | 10 | entropy | 0.823821 | 0.952144 |
| 1082 | 0.250000 | 13 | 10 | log_loss | 0.774194 | 0.935985 |
| 1083 | 0.250000 | 13 | 50 | gini | 0.861042 | 0.965196 |
| 1084 | 0.250000 | 13 | 50 | entropy | 0.863524 | 0.965196 |
| 1085 | 0.250000 | 13 | 50 | log_loss | 0.875931 | 0.968925 |
| 1086 | 0.250000 | 13 | 100 | gini | 0.883375 | 0.970789 |
| 1087 | 0.250000 | 13 | 100 | entropy | 0.873449 | 0.968303 |
| 1088 | 0.250000 | 13 | 100 | log_loss | 0.888337 | 0.972032 |
| 1089 | 0.250000 | 13 | 200 | gini | 0.870968 | 0.967682 |
| 1090 | 0.250000 | 13 | 200 | entropy | 0.875931 | 0.968925 |
| 1091 | 0.250000 | 13 | 200 | log_loss | 0.880893 | 0.970168 |
| 1092 | 0.250000 | 13 | 300 | gini | 0.866005 | 0.966439 |
| 1093 | 0.250000 | 13 | 300 | entropy | 0.873449 | 0.968303 |
| 1094 | 0.250000 | 13 | 300 | log_loss | 0.873449 | 0.968303 |
| 1095 | 0.250000 | 14 | 10 | gini | 0.801489 | 0.945929 |
| 1096 | 0.250000 | 14 | 10 | entropy | 0.861042 | 0.961467 |
| 1097 | 0.250000 | 14 | 10 | log_loss | 0.779156 | 0.942200 |
| 1098 | 0.250000 | 14 | 50 | gini | 0.851117 | 0.962710 |
| 1099 | 0.250000 | 14 | 50 | entropy | 0.863524 | 0.965817 |
| 1100 | 0.250000 | 14 | 50 | log_loss | 0.846154 | 0.961467 |
| 1101 | 0.250000 | 14 | 100 | gini | 0.880893 | 0.970168 |
| 1102 | 0.250000 | 14 | 100 | entropy | 0.868486 | 0.967060 |
| 1103 | 0.250000 | 14 | 100 | log_loss | 0.880893 | 0.970168 |
| 1104 | 0.250000 | 14 | 200 | gini | 0.866005 | 0.966439 |
| 1105 | 0.250000 | 14 | 200 | entropy | 0.890819 | 0.972654 |
| 1106 | 0.250000 | 14 | 200 | log_loss | 0.873449 | 0.968303 |
| 1107 | 0.250000 | 14 | 300 | gini | 0.870968 | 0.967682 |
| 1108 | 0.250000 | 14 | 300 | entropy | 0.878412 | 0.969546 |
| 1109 | 0.250000 | 14 | 300 | log_loss | 0.883375 | 0.970789 |
| 1110 | 0.250000 | 15 | 10 | gini | 0.794045 | 0.943443 |
| 1111 | 0.250000 | 15 | 10 | entropy | 0.818859 | 0.950901 |
| 1112 | 0.250000 | 15 | 10 | log_loss | 0.789082 | 0.944065 |
| 1113 | 0.250000 | 15 | 50 | gini | 0.841191 | 0.960224 |
| 1114 | 0.250000 | 15 | 50 | entropy | 0.878412 | 0.969546 |
| 1115 | 0.250000 | 15 | 50 | log_loss | 0.866005 | 0.966439 |
| 1116 | 0.250000 | 15 | 100 | gini | 0.878412 | 0.969546 |
| 1117 | 0.250000 | 15 | 100 | entropy | 0.848635 | 0.962088 |
| 1118 | 0.250000 | 15 | 100 | log_loss | 0.866005 | 0.966439 |
| 1119 | 0.250000 | 15 | 200 | gini | 0.870968 | 0.967682 |
| 1120 | 0.250000 | 15 | 200 | entropy | 0.873449 | 0.968303 |
| 1121 | 0.250000 | 15 | 200 | log_loss | 0.878412 | 0.969546 |
| 1122 | 0.250000 | 15 | 300 | gini | 0.873449 | 0.968303 |
| 1123 | 0.250000 | 15 | 300 | entropy | 0.873449 | 0.968303 |
| 1124 | 0.250000 | 15 | 300 | log_loss | 0.883375 | 0.970789 |
| 1125 | 0.250000 | 16 | 10 | gini | 0.794045 | 0.944065 |
| 1126 | 0.250000 | 16 | 10 | entropy | 0.806452 | 0.947172 |
| 1127 | 0.250000 | 16 | 10 | log_loss | 0.826303 | 0.954009 |
| 1128 | 0.250000 | 16 | 50 | gini | 0.883375 | 0.970789 |
| 1129 | 0.250000 | 16 | 50 | entropy | 0.875931 | 0.968925 |
| 1130 | 0.250000 | 16 | 50 | log_loss | 0.853598 | 0.963331 |
| 1131 | 0.250000 | 16 | 100 | gini | 0.873449 | 0.968303 |
| 1132 | 0.250000 | 16 | 100 | entropy | 0.883375 | 0.970789 |
| 1133 | 0.250000 | 16 | 100 | log_loss | 0.880893 | 0.970168 |
| 1134 | 0.250000 | 16 | 200 | gini | 0.883375 | 0.970789 |
| 1135 | 0.250000 | 16 | 200 | entropy | 0.883375 | 0.970789 |
| 1136 | 0.250000 | 16 | 200 | log_loss | 0.873449 | 0.968303 |
| 1137 | 0.250000 | 16 | 300 | gini | 0.866005 | 0.966439 |
| 1138 | 0.250000 | 16 | 300 | entropy | 0.878412 | 0.969546 |
| 1139 | 0.250000 | 16 | 300 | log_loss | 0.888337 | 0.972032 |
| 1140 | 0.250000 | 17 | 10 | gini | 0.801489 | 0.947794 |
| 1141 | 0.250000 | 17 | 10 | entropy | 0.786600 | 0.944065 |
| 1142 | 0.250000 | 17 | 10 | log_loss | 0.803970 | 0.947172 |
| 1143 | 0.250000 | 17 | 50 | gini | 0.836228 | 0.958981 |
| 1144 | 0.250000 | 17 | 50 | entropy | 0.873449 | 0.968303 |
| 1145 | 0.250000 | 17 | 50 | log_loss | 0.898263 | 0.974518 |
| 1146 | 0.250000 | 17 | 100 | gini | 0.856079 | 0.963953 |
| 1147 | 0.250000 | 17 | 100 | entropy | 0.885856 | 0.971411 |
| 1148 | 0.250000 | 17 | 100 | log_loss | 0.878412 | 0.969546 |
| 1149 | 0.250000 | 17 | 200 | gini | 0.880893 | 0.970168 |
| 1150 | 0.250000 | 17 | 200 | entropy | 0.883375 | 0.970789 |
| 1151 | 0.250000 | 17 | 200 | log_loss | 0.880893 | 0.970168 |
| 1152 | 0.250000 | 17 | 300 | gini | 0.868486 | 0.967060 |
| 1153 | 0.250000 | 17 | 300 | entropy | 0.878412 | 0.969546 |
| 1154 | 0.250000 | 17 | 300 | log_loss | 0.875931 | 0.968925 |
| 1155 | 0.250000 | 18 | 10 | gini | 0.786600 | 0.944065 |
| 1156 | 0.250000 | 18 | 10 | entropy | 0.833747 | 0.955252 |
| 1157 | 0.250000 | 18 | 10 | log_loss | 0.794045 | 0.946551 |
| 1158 | 0.250000 | 18 | 50 | gini | 0.866005 | 0.966439 |
| 1159 | 0.250000 | 18 | 50 | entropy | 0.868486 | 0.967060 |
| 1160 | 0.250000 | 18 | 50 | log_loss | 0.856079 | 0.963953 |
| 1161 | 0.250000 | 18 | 100 | gini | 0.880893 | 0.970168 |
| 1162 | 0.250000 | 18 | 100 | entropy | 0.868486 | 0.967060 |
| 1163 | 0.250000 | 18 | 100 | log_loss | 0.870968 | 0.967682 |
| 1164 | 0.250000 | 18 | 200 | gini | 0.880893 | 0.970168 |
| 1165 | 0.250000 | 18 | 200 | entropy | 0.878412 | 0.969546 |
| 1166 | 0.250000 | 18 | 200 | log_loss | 0.873449 | 0.968303 |
| 1167 | 0.250000 | 18 | 300 | gini | 0.878412 | 0.969546 |
| 1168 | 0.250000 | 18 | 300 | entropy | 0.883375 | 0.970789 |
| 1169 | 0.250000 | 18 | 300 | log_loss | 0.875931 | 0.968925 |
| 1170 | 0.250000 | 19 | 10 | gini | 0.756824 | 0.935364 |
| 1171 | 0.250000 | 19 | 10 | entropy | 0.826303 | 0.951523 |
| 1172 | 0.250000 | 19 | 10 | log_loss | 0.831266 | 0.956495 |
| 1173 | 0.250000 | 19 | 50 | gini | 0.866005 | 0.966439 |
| 1174 | 0.250000 | 19 | 50 | entropy | 0.863524 | 0.965817 |
| 1175 | 0.250000 | 19 | 50 | log_loss | 0.863524 | 0.965817 |
| 1176 | 0.250000 | 19 | 100 | gini | 0.853598 | 0.963331 |
| 1177 | 0.250000 | 19 | 100 | entropy | 0.861042 | 0.965196 |
| 1178 | 0.250000 | 19 | 100 | log_loss | 0.878412 | 0.969546 |
| 1179 | 0.250000 | 19 | 200 | gini | 0.878412 | 0.969546 |
| 1180 | 0.250000 | 19 | 200 | entropy | 0.868486 | 0.967060 |
| 1181 | 0.250000 | 19 | 200 | log_loss | 0.875931 | 0.968925 |
| 1182 | 0.250000 | 19 | 300 | gini | 0.878412 | 0.969546 |
| 1183 | 0.250000 | 19 | 300 | entropy | 0.878412 | 0.969546 |
| 1184 | 0.250000 | 19 | 300 | log_loss | 0.868486 | 0.967060 |
| 1185 | 0.250000 | 20 | 10 | gini | 0.744417 | 0.934742 |
| 1186 | 0.250000 | 20 | 10 | entropy | 0.813896 | 0.951523 |
| 1187 | 0.250000 | 20 | 10 | log_loss | 0.781638 | 0.941579 |
| 1188 | 0.250000 | 20 | 50 | gini | 0.868486 | 0.967060 |
| 1189 | 0.250000 | 20 | 50 | entropy | 0.868486 | 0.967060 |
| 1190 | 0.250000 | 20 | 50 | log_loss | 0.863524 | 0.965817 |
| 1191 | 0.250000 | 20 | 100 | gini | 0.858561 | 0.964574 |
| 1192 | 0.250000 | 20 | 100 | entropy | 0.868486 | 0.967060 |
| 1193 | 0.250000 | 20 | 100 | log_loss | 0.883375 | 0.970789 |
| 1194 | 0.250000 | 20 | 200 | gini | 0.866005 | 0.966439 |
| 1195 | 0.250000 | 20 | 200 | entropy | 0.868486 | 0.967060 |
| 1196 | 0.250000 | 20 | 200 | log_loss | 0.890819 | 0.972654 |
| 1197 | 0.250000 | 20 | 300 | gini | 0.868486 | 0.967060 |
| 1198 | 0.250000 | 20 | 300 | entropy | 0.878412 | 0.969546 |
| 1199 | 0.250000 | 20 | 300 | log_loss | 0.885856 | 0.971411 |
| 1200 | 0.300000 | 1 | 10 | gini | 0.525880 | 0.545681 |
| 1201 | 0.300000 | 1 | 10 | entropy | 0.654244 | 0.668738 |
| 1202 | 0.300000 | 1 | 10 | log_loss | 0.509317 | 0.517091 |
| 1203 | 0.300000 | 1 | 50 | gini | 0.614907 | 0.653201 |
| 1204 | 0.300000 | 1 | 50 | entropy | 0.573499 | 0.582349 |
| 1205 | 0.300000 | 1 | 50 | log_loss | 0.550725 | 0.553139 |
| 1206 | 0.300000 | 1 | 100 | gini | 0.679089 | 0.696085 |
| 1207 | 0.300000 | 1 | 100 | entropy | 0.587992 | 0.596644 |
| 1208 | 0.300000 | 1 | 100 | log_loss | 0.577640 | 0.576756 |
| 1209 | 0.300000 | 1 | 200 | gini | 0.654244 | 0.672467 |
| 1210 | 0.300000 | 1 | 200 | entropy | 0.573499 | 0.583592 |
| 1211 | 0.300000 | 1 | 200 | log_loss | 0.565217 | 0.559975 |
| 1212 | 0.300000 | 1 | 300 | gini | 0.706004 | 0.714108 |
| 1213 | 0.300000 | 1 | 300 | entropy | 0.616977 | 0.610317 |
| 1214 | 0.300000 | 1 | 300 | log_loss | 0.585921 | 0.592915 |
| 1215 | 0.300000 | 2 | 10 | gini | 0.598344 | 0.617154 |
| 1216 | 0.300000 | 2 | 10 | entropy | 0.544513 | 0.556868 |
| 1217 | 0.300000 | 2 | 10 | log_loss | 0.662526 | 0.678061 |
| 1218 | 0.300000 | 2 | 50 | gini | 0.737060 | 0.740211 |
| 1219 | 0.300000 | 2 | 50 | entropy | 0.755694 | 0.766936 |
| 1220 | 0.300000 | 2 | 50 | log_loss | 0.643892 | 0.668738 |
| 1221 | 0.300000 | 2 | 100 | gini | 0.788820 | 0.778745 |
| 1222 | 0.300000 | 2 | 100 | entropy | 0.751553 | 0.766936 |
| 1223 | 0.300000 | 2 | 100 | log_loss | 0.724638 | 0.735861 |
| 1224 | 0.300000 | 2 | 200 | gini | 0.792961 | 0.806712 |
| 1225 | 0.300000 | 2 | 200 | entropy | 0.693582 | 0.711001 |
| 1226 | 0.300000 | 2 | 200 | log_loss | 0.774327 | 0.790553 |
| 1227 | 0.300000 | 2 | 300 | gini | 0.774327 | 0.778123 |
| 1228 | 0.300000 | 2 | 300 | entropy | 0.743271 | 0.738968 |
| 1229 | 0.300000 | 2 | 300 | log_loss | 0.761905 | 0.761964 |
| 1230 | 0.300000 | 3 | 10 | gini | 0.710145 | 0.768179 |
| 1231 | 0.300000 | 3 | 10 | entropy | 0.629400 | 0.656308 |
| 1232 | 0.300000 | 3 | 10 | log_loss | 0.693582 | 0.717216 |
| 1233 | 0.300000 | 3 | 50 | gini | 0.815735 | 0.832194 |
| 1234 | 0.300000 | 3 | 50 | entropy | 0.811594 | 0.817899 |
| 1235 | 0.300000 | 3 | 50 | log_loss | 0.768116 | 0.800497 |
| 1236 | 0.300000 | 3 | 100 | gini | 0.790890 | 0.824736 |
| 1237 | 0.300000 | 3 | 100 | entropy | 0.811594 | 0.830329 |
| 1238 | 0.300000 | 3 | 100 | log_loss | 0.815735 | 0.840273 |
| 1239 | 0.300000 | 3 | 200 | gini | 0.824017 | 0.842759 |
| 1240 | 0.300000 | 3 | 200 | entropy | 0.813665 | 0.839652 |
| 1241 | 0.300000 | 3 | 200 | log_loss | 0.828157 | 0.844002 |
| 1242 | 0.300000 | 3 | 300 | gini | 0.826087 | 0.840895 |
| 1243 | 0.300000 | 3 | 300 | entropy | 0.805383 | 0.825979 |
| 1244 | 0.300000 | 3 | 300 | log_loss | 0.815735 | 0.838409 |
| 1245 | 0.300000 | 4 | 10 | gini | 0.734990 | 0.766314 |
| 1246 | 0.300000 | 4 | 10 | entropy | 0.759834 | 0.801740 |
| 1247 | 0.300000 | 4 | 10 | log_loss | 0.639752 | 0.721566 |
| 1248 | 0.300000 | 4 | 50 | gini | 0.828157 | 0.860783 |
| 1249 | 0.300000 | 4 | 50 | entropy | 0.826087 | 0.869484 |
| 1250 | 0.300000 | 4 | 50 | log_loss | 0.795031 | 0.851461 |
| 1251 | 0.300000 | 4 | 100 | gini | 0.834369 | 0.868863 |
| 1252 | 0.300000 | 4 | 100 | entropy | 0.836439 | 0.867620 |
| 1253 | 0.300000 | 4 | 100 | log_loss | 0.836439 | 0.869484 |
| 1254 | 0.300000 | 4 | 200 | gini | 0.826087 | 0.868863 |
| 1255 | 0.300000 | 4 | 200 | entropy | 0.836439 | 0.868863 |
| 1256 | 0.300000 | 4 | 200 | log_loss | 0.846791 | 0.872592 |
| 1257 | 0.300000 | 4 | 300 | gini | 0.840580 | 0.875699 |
| 1258 | 0.300000 | 4 | 300 | entropy | 0.844720 | 0.868241 |
| 1259 | 0.300000 | 4 | 300 | log_loss | 0.848861 | 0.879428 |
| 1260 | 0.300000 | 5 | 10 | gini | 0.786749 | 0.855811 |
| 1261 | 0.300000 | 5 | 10 | entropy | 0.813665 | 0.852704 |
| 1262 | 0.300000 | 5 | 10 | log_loss | 0.751553 | 0.816656 |
| 1263 | 0.300000 | 5 | 50 | gini | 0.834369 | 0.892480 |
| 1264 | 0.300000 | 5 | 50 | entropy | 0.834369 | 0.890615 |
| 1265 | 0.300000 | 5 | 50 | log_loss | 0.834369 | 0.896209 |
| 1266 | 0.300000 | 5 | 100 | gini | 0.832298 | 0.907396 |
| 1267 | 0.300000 | 5 | 100 | entropy | 0.842650 | 0.893101 |
| 1268 | 0.300000 | 5 | 100 | log_loss | 0.834369 | 0.894966 |
| 1269 | 0.300000 | 5 | 200 | gini | 0.844720 | 0.909882 |
| 1270 | 0.300000 | 5 | 200 | entropy | 0.836439 | 0.890615 |
| 1271 | 0.300000 | 5 | 200 | log_loss | 0.861284 | 0.904910 |
| 1272 | 0.300000 | 5 | 300 | gini | 0.850932 | 0.906153 |
| 1273 | 0.300000 | 5 | 300 | entropy | 0.848861 | 0.894966 |
| 1274 | 0.300000 | 5 | 300 | log_loss | 0.848861 | 0.901802 |
| 1275 | 0.300000 | 6 | 10 | gini | 0.757764 | 0.867620 |
| 1276 | 0.300000 | 6 | 10 | entropy | 0.770186 | 0.854568 |
| 1277 | 0.300000 | 6 | 10 | log_loss | 0.780538 | 0.852704 |
| 1278 | 0.300000 | 6 | 50 | gini | 0.842650 | 0.917961 |
| 1279 | 0.300000 | 6 | 50 | entropy | 0.840580 | 0.916718 |
| 1280 | 0.300000 | 6 | 50 | log_loss | 0.863354 | 0.927906 |
| 1281 | 0.300000 | 6 | 100 | gini | 0.869565 | 0.931635 |
| 1282 | 0.300000 | 6 | 100 | entropy | 0.865424 | 0.934742 |
| 1283 | 0.300000 | 6 | 100 | log_loss | 0.853002 | 0.927906 |
| 1284 | 0.300000 | 6 | 200 | gini | 0.850932 | 0.925420 |
| 1285 | 0.300000 | 6 | 200 | entropy | 0.857143 | 0.926041 |
| 1286 | 0.300000 | 6 | 200 | log_loss | 0.857143 | 0.931635 |
| 1287 | 0.300000 | 6 | 300 | gini | 0.861284 | 0.931635 |
| 1288 | 0.300000 | 6 | 300 | entropy | 0.850932 | 0.925420 |
| 1289 | 0.300000 | 6 | 300 | log_loss | 0.873706 | 0.937228 |
| 1290 | 0.300000 | 7 | 10 | gini | 0.770186 | 0.881914 |
| 1291 | 0.300000 | 7 | 10 | entropy | 0.774327 | 0.898073 |
| 1292 | 0.300000 | 7 | 10 | log_loss | 0.747412 | 0.898073 |
| 1293 | 0.300000 | 7 | 50 | gini | 0.865424 | 0.945929 |
| 1294 | 0.300000 | 7 | 50 | entropy | 0.840580 | 0.939093 |
| 1295 | 0.300000 | 7 | 50 | log_loss | 0.848861 | 0.934742 |
| 1296 | 0.300000 | 7 | 100 | gini | 0.869565 | 0.947794 |
| 1297 | 0.300000 | 7 | 100 | entropy | 0.859213 | 0.945929 |
| 1298 | 0.300000 | 7 | 100 | log_loss | 0.861284 | 0.945308 |
| 1299 | 0.300000 | 7 | 200 | gini | 0.871636 | 0.950280 |
| 1300 | 0.300000 | 7 | 200 | entropy | 0.863354 | 0.947172 |
| 1301 | 0.300000 | 7 | 200 | log_loss | 0.875776 | 0.949658 |
| 1302 | 0.300000 | 7 | 300 | gini | 0.873706 | 0.952144 |
| 1303 | 0.300000 | 7 | 300 | entropy | 0.863354 | 0.946551 |
| 1304 | 0.300000 | 7 | 300 | log_loss | 0.861284 | 0.949037 |
| 1305 | 0.300000 | 8 | 10 | gini | 0.780538 | 0.909882 |
| 1306 | 0.300000 | 8 | 10 | entropy | 0.788820 | 0.919826 |
| 1307 | 0.300000 | 8 | 10 | log_loss | 0.786749 | 0.907396 |
| 1308 | 0.300000 | 8 | 50 | gini | 0.844720 | 0.947172 |
| 1309 | 0.300000 | 8 | 50 | entropy | 0.865424 | 0.955252 |
| 1310 | 0.300000 | 8 | 50 | log_loss | 0.863354 | 0.953387 |
| 1311 | 0.300000 | 8 | 100 | gini | 0.863354 | 0.952766 |
| 1312 | 0.300000 | 8 | 100 | entropy | 0.892340 | 0.965196 |
| 1313 | 0.300000 | 8 | 100 | log_loss | 0.873706 | 0.959602 |
| 1314 | 0.300000 | 8 | 200 | gini | 0.861284 | 0.954009 |
| 1315 | 0.300000 | 8 | 200 | entropy | 0.875776 | 0.958981 |
| 1316 | 0.300000 | 8 | 200 | log_loss | 0.867495 | 0.958981 |
| 1317 | 0.300000 | 8 | 300 | gini | 0.873706 | 0.957738 |
| 1318 | 0.300000 | 8 | 300 | entropy | 0.871636 | 0.958981 |
| 1319 | 0.300000 | 8 | 300 | log_loss | 0.879917 | 0.962710 |
| 1320 | 0.300000 | 9 | 10 | gini | 0.799172 | 0.922312 |
| 1321 | 0.300000 | 9 | 10 | entropy | 0.830228 | 0.939093 |
| 1322 | 0.300000 | 9 | 10 | log_loss | 0.795031 | 0.922933 |
| 1323 | 0.300000 | 9 | 50 | gini | 0.869565 | 0.959602 |
| 1324 | 0.300000 | 9 | 50 | entropy | 0.877847 | 0.962710 |
| 1325 | 0.300000 | 9 | 50 | log_loss | 0.857143 | 0.957116 |
| 1326 | 0.300000 | 9 | 100 | gini | 0.848861 | 0.954009 |
| 1327 | 0.300000 | 9 | 100 | entropy | 0.881988 | 0.964574 |
| 1328 | 0.300000 | 9 | 100 | log_loss | 0.879917 | 0.963331 |
| 1329 | 0.300000 | 9 | 200 | gini | 0.871636 | 0.961467 |
| 1330 | 0.300000 | 9 | 200 | entropy | 0.886128 | 0.964574 |
| 1331 | 0.300000 | 9 | 200 | log_loss | 0.871636 | 0.961467 |
| 1332 | 0.300000 | 9 | 300 | gini | 0.875776 | 0.962088 |
| 1333 | 0.300000 | 9 | 300 | entropy | 0.873706 | 0.962088 |
| 1334 | 0.300000 | 9 | 300 | log_loss | 0.875776 | 0.962710 |
| 1335 | 0.300000 | 10 | 10 | gini | 0.809524 | 0.931635 |
| 1336 | 0.300000 | 10 | 10 | entropy | 0.840580 | 0.947172 |
| 1337 | 0.300000 | 10 | 10 | log_loss | 0.768116 | 0.916718 |
| 1338 | 0.300000 | 10 | 50 | gini | 0.877847 | 0.962710 |
| 1339 | 0.300000 | 10 | 50 | entropy | 0.871636 | 0.961467 |
| 1340 | 0.300000 | 10 | 50 | log_loss | 0.857143 | 0.957116 |
| 1341 | 0.300000 | 10 | 100 | gini | 0.869565 | 0.960224 |
| 1342 | 0.300000 | 10 | 100 | entropy | 0.877847 | 0.963331 |
| 1343 | 0.300000 | 10 | 100 | log_loss | 0.881988 | 0.964574 |
| 1344 | 0.300000 | 10 | 200 | gini | 0.881988 | 0.964574 |
| 1345 | 0.300000 | 10 | 200 | entropy | 0.881988 | 0.964574 |
| 1346 | 0.300000 | 10 | 200 | log_loss | 0.886128 | 0.965817 |
| 1347 | 0.300000 | 10 | 300 | gini | 0.875776 | 0.962710 |
| 1348 | 0.300000 | 10 | 300 | entropy | 0.875776 | 0.962710 |
| 1349 | 0.300000 | 10 | 300 | log_loss | 0.881988 | 0.964574 |
| 1350 | 0.300000 | 11 | 10 | gini | 0.782609 | 0.931013 |
| 1351 | 0.300000 | 11 | 10 | entropy | 0.824017 | 0.940957 |
| 1352 | 0.300000 | 11 | 10 | log_loss | 0.795031 | 0.933499 |
| 1353 | 0.300000 | 11 | 50 | gini | 0.873706 | 0.962088 |
| 1354 | 0.300000 | 11 | 50 | entropy | 0.873706 | 0.962088 |
| 1355 | 0.300000 | 11 | 50 | log_loss | 0.865424 | 0.959602 |
| 1356 | 0.300000 | 11 | 100 | gini | 0.890269 | 0.967060 |
| 1357 | 0.300000 | 11 | 100 | entropy | 0.881988 | 0.964574 |
| 1358 | 0.300000 | 11 | 100 | log_loss | 0.881988 | 0.964574 |
| 1359 | 0.300000 | 11 | 200 | gini | 0.884058 | 0.965196 |
| 1360 | 0.300000 | 11 | 200 | entropy | 0.896480 | 0.968925 |
| 1361 | 0.300000 | 11 | 200 | log_loss | 0.890269 | 0.967060 |
| 1362 | 0.300000 | 11 | 300 | gini | 0.877847 | 0.963331 |
| 1363 | 0.300000 | 11 | 300 | entropy | 0.873706 | 0.962088 |
| 1364 | 0.300000 | 11 | 300 | log_loss | 0.886128 | 0.965817 |
| 1365 | 0.300000 | 12 | 10 | gini | 0.737060 | 0.913611 |
| 1366 | 0.300000 | 12 | 10 | entropy | 0.805383 | 0.939714 |
| 1367 | 0.300000 | 12 | 10 | log_loss | 0.786749 | 0.931013 |
| 1368 | 0.300000 | 12 | 50 | gini | 0.859213 | 0.957738 |
| 1369 | 0.300000 | 12 | 50 | entropy | 0.873706 | 0.962088 |
| 1370 | 0.300000 | 12 | 50 | log_loss | 0.869565 | 0.960845 |
| 1371 | 0.300000 | 12 | 100 | gini | 0.884058 | 0.965196 |
| 1372 | 0.300000 | 12 | 100 | entropy | 0.896480 | 0.968925 |
| 1373 | 0.300000 | 12 | 100 | log_loss | 0.894410 | 0.968303 |
| 1374 | 0.300000 | 12 | 200 | gini | 0.873706 | 0.962088 |
| 1375 | 0.300000 | 12 | 200 | entropy | 0.879917 | 0.963953 |
| 1376 | 0.300000 | 12 | 200 | log_loss | 0.888199 | 0.966439 |
| 1377 | 0.300000 | 12 | 300 | gini | 0.873706 | 0.962088 |
| 1378 | 0.300000 | 12 | 300 | entropy | 0.873706 | 0.962088 |
| 1379 | 0.300000 | 12 | 300 | log_loss | 0.890269 | 0.967060 |
| 1380 | 0.300000 | 13 | 10 | gini | 0.815735 | 0.942200 |
| 1381 | 0.300000 | 13 | 10 | entropy | 0.805383 | 0.940957 |
| 1382 | 0.300000 | 13 | 10 | log_loss | 0.786749 | 0.931635 |
| 1383 | 0.300000 | 13 | 50 | gini | 0.859213 | 0.957738 |
| 1384 | 0.300000 | 13 | 50 | entropy | 0.846791 | 0.954009 |
| 1385 | 0.300000 | 13 | 50 | log_loss | 0.869565 | 0.960845 |
| 1386 | 0.300000 | 13 | 100 | gini | 0.861284 | 0.958359 |
| 1387 | 0.300000 | 13 | 100 | entropy | 0.869565 | 0.960845 |
| 1388 | 0.300000 | 13 | 100 | log_loss | 0.892340 | 0.967682 |
| 1389 | 0.300000 | 13 | 200 | gini | 0.890269 | 0.967060 |
| 1390 | 0.300000 | 13 | 200 | entropy | 0.890269 | 0.967060 |
| 1391 | 0.300000 | 13 | 200 | log_loss | 0.896480 | 0.968925 |
| 1392 | 0.300000 | 13 | 300 | gini | 0.888199 | 0.966439 |
| 1393 | 0.300000 | 13 | 300 | entropy | 0.884058 | 0.965196 |
| 1394 | 0.300000 | 13 | 300 | log_loss | 0.892340 | 0.967682 |
| 1395 | 0.300000 | 14 | 10 | gini | 0.817805 | 0.940957 |
| 1396 | 0.300000 | 14 | 10 | entropy | 0.770186 | 0.928527 |
| 1397 | 0.300000 | 14 | 10 | log_loss | 0.838509 | 0.948415 |
| 1398 | 0.300000 | 14 | 50 | gini | 0.853002 | 0.955873 |
| 1399 | 0.300000 | 14 | 50 | entropy | 0.863354 | 0.958981 |
| 1400 | 0.300000 | 14 | 50 | log_loss | 0.865424 | 0.959602 |
| 1401 | 0.300000 | 14 | 100 | gini | 0.867495 | 0.960224 |
| 1402 | 0.300000 | 14 | 100 | entropy | 0.869565 | 0.960845 |
| 1403 | 0.300000 | 14 | 100 | log_loss | 0.886128 | 0.965817 |
| 1404 | 0.300000 | 14 | 200 | gini | 0.892340 | 0.967682 |
| 1405 | 0.300000 | 14 | 200 | entropy | 0.884058 | 0.965196 |
| 1406 | 0.300000 | 14 | 200 | log_loss | 0.892340 | 0.967682 |
| 1407 | 0.300000 | 14 | 300 | gini | 0.886128 | 0.965817 |
| 1408 | 0.300000 | 14 | 300 | entropy | 0.881988 | 0.964574 |
| 1409 | 0.300000 | 14 | 300 | log_loss | 0.884058 | 0.965196 |
| 1410 | 0.300000 | 15 | 10 | gini | 0.826087 | 0.945308 |
| 1411 | 0.300000 | 15 | 10 | entropy | 0.792961 | 0.935364 |
| 1412 | 0.300000 | 15 | 10 | log_loss | 0.797101 | 0.937850 |
| 1413 | 0.300000 | 15 | 50 | gini | 0.865424 | 0.959602 |
| 1414 | 0.300000 | 15 | 50 | entropy | 0.857143 | 0.957116 |
| 1415 | 0.300000 | 15 | 50 | log_loss | 0.877847 | 0.963331 |
| 1416 | 0.300000 | 15 | 100 | gini | 0.867495 | 0.960224 |
| 1417 | 0.300000 | 15 | 100 | entropy | 0.888199 | 0.966439 |
| 1418 | 0.300000 | 15 | 100 | log_loss | 0.873706 | 0.962088 |
| 1419 | 0.300000 | 15 | 200 | gini | 0.896480 | 0.968925 |
| 1420 | 0.300000 | 15 | 200 | entropy | 0.888199 | 0.966439 |
| 1421 | 0.300000 | 15 | 200 | log_loss | 0.879917 | 0.963953 |
| 1422 | 0.300000 | 15 | 300 | gini | 0.892340 | 0.967682 |
| 1423 | 0.300000 | 15 | 300 | entropy | 0.877847 | 0.963331 |
| 1424 | 0.300000 | 15 | 300 | log_loss | 0.888199 | 0.966439 |
| 1425 | 0.300000 | 16 | 10 | gini | 0.772257 | 0.929770 |
| 1426 | 0.300000 | 16 | 10 | entropy | 0.815735 | 0.942822 |
| 1427 | 0.300000 | 16 | 10 | log_loss | 0.803313 | 0.936607 |
| 1428 | 0.300000 | 16 | 50 | gini | 0.859213 | 0.957738 |
| 1429 | 0.300000 | 16 | 50 | entropy | 0.884058 | 0.965196 |
| 1430 | 0.300000 | 16 | 50 | log_loss | 0.886128 | 0.965817 |
| 1431 | 0.300000 | 16 | 100 | gini | 0.884058 | 0.965196 |
| 1432 | 0.300000 | 16 | 100 | entropy | 0.873706 | 0.962088 |
| 1433 | 0.300000 | 16 | 100 | log_loss | 0.890269 | 0.967060 |
| 1434 | 0.300000 | 16 | 200 | gini | 0.881988 | 0.964574 |
| 1435 | 0.300000 | 16 | 200 | entropy | 0.890269 | 0.967060 |
| 1436 | 0.300000 | 16 | 200 | log_loss | 0.879917 | 0.963953 |
| 1437 | 0.300000 | 16 | 300 | gini | 0.884058 | 0.965196 |
| 1438 | 0.300000 | 16 | 300 | entropy | 0.884058 | 0.965196 |
| 1439 | 0.300000 | 16 | 300 | log_loss | 0.886128 | 0.965817 |
| 1440 | 0.300000 | 17 | 10 | gini | 0.788820 | 0.934121 |
| 1441 | 0.300000 | 17 | 10 | entropy | 0.799172 | 0.937850 |
| 1442 | 0.300000 | 17 | 10 | log_loss | 0.786749 | 0.933499 |
| 1443 | 0.300000 | 17 | 50 | gini | 0.871636 | 0.961467 |
| 1444 | 0.300000 | 17 | 50 | entropy | 0.879917 | 0.963953 |
| 1445 | 0.300000 | 17 | 50 | log_loss | 0.881988 | 0.964574 |
| 1446 | 0.300000 | 17 | 100 | gini | 0.884058 | 0.965196 |
| 1447 | 0.300000 | 17 | 100 | entropy | 0.875776 | 0.962710 |
| 1448 | 0.300000 | 17 | 100 | log_loss | 0.890269 | 0.967060 |
| 1449 | 0.300000 | 17 | 200 | gini | 0.869565 | 0.960845 |
| 1450 | 0.300000 | 17 | 200 | entropy | 0.884058 | 0.965196 |
| 1451 | 0.300000 | 17 | 200 | log_loss | 0.871636 | 0.961467 |
| 1452 | 0.300000 | 17 | 300 | gini | 0.877847 | 0.963331 |
| 1453 | 0.300000 | 17 | 300 | entropy | 0.892340 | 0.967682 |
| 1454 | 0.300000 | 17 | 300 | log_loss | 0.881988 | 0.964574 |
| 1455 | 0.300000 | 18 | 10 | gini | 0.803313 | 0.939714 |
| 1456 | 0.300000 | 18 | 10 | entropy | 0.792961 | 0.932878 |
| 1457 | 0.300000 | 18 | 10 | log_loss | 0.782609 | 0.930392 |
| 1458 | 0.300000 | 18 | 50 | gini | 0.863354 | 0.958981 |
| 1459 | 0.300000 | 18 | 50 | entropy | 0.892340 | 0.967682 |
| 1460 | 0.300000 | 18 | 50 | log_loss | 0.877847 | 0.963331 |
| 1461 | 0.300000 | 18 | 100 | gini | 0.871636 | 0.961467 |
| 1462 | 0.300000 | 18 | 100 | entropy | 0.879917 | 0.963953 |
| 1463 | 0.300000 | 18 | 100 | log_loss | 0.857143 | 0.957116 |
| 1464 | 0.300000 | 18 | 200 | gini | 0.871636 | 0.961467 |
| 1465 | 0.300000 | 18 | 200 | entropy | 0.892340 | 0.967682 |
| 1466 | 0.300000 | 18 | 200 | log_loss | 0.894410 | 0.968303 |
| 1467 | 0.300000 | 18 | 300 | gini | 0.879917 | 0.963953 |
| 1468 | 0.300000 | 18 | 300 | entropy | 0.890269 | 0.967060 |
| 1469 | 0.300000 | 18 | 300 | log_loss | 0.890269 | 0.967060 |
| 1470 | 0.300000 | 19 | 10 | gini | 0.782609 | 0.932256 |
| 1471 | 0.300000 | 19 | 10 | entropy | 0.815735 | 0.941579 |
| 1472 | 0.300000 | 19 | 10 | log_loss | 0.792961 | 0.937850 |
| 1473 | 0.300000 | 19 | 50 | gini | 0.871636 | 0.961467 |
| 1474 | 0.300000 | 19 | 50 | entropy | 0.869565 | 0.960845 |
| 1475 | 0.300000 | 19 | 50 | log_loss | 0.848861 | 0.954630 |
| 1476 | 0.300000 | 19 | 100 | gini | 0.867495 | 0.960224 |
| 1477 | 0.300000 | 19 | 100 | entropy | 0.890269 | 0.967060 |
| 1478 | 0.300000 | 19 | 100 | log_loss | 0.865424 | 0.959602 |
| 1479 | 0.300000 | 19 | 200 | gini | 0.867495 | 0.960224 |
| 1480 | 0.300000 | 19 | 200 | entropy | 0.890269 | 0.967060 |
| 1481 | 0.300000 | 19 | 200 | log_loss | 0.892340 | 0.967682 |
| 1482 | 0.300000 | 19 | 300 | gini | 0.877847 | 0.963331 |
| 1483 | 0.300000 | 19 | 300 | entropy | 0.890269 | 0.967060 |
| 1484 | 0.300000 | 19 | 300 | log_loss | 0.888199 | 0.966439 |
| 1485 | 0.300000 | 20 | 10 | gini | 0.788820 | 0.933499 |
| 1486 | 0.300000 | 20 | 10 | entropy | 0.768116 | 0.927284 |
| 1487 | 0.300000 | 20 | 10 | log_loss | 0.805383 | 0.939714 |
| 1488 | 0.300000 | 20 | 50 | gini | 0.873706 | 0.962088 |
| 1489 | 0.300000 | 20 | 50 | entropy | 0.873706 | 0.962088 |
| 1490 | 0.300000 | 20 | 50 | log_loss | 0.865424 | 0.959602 |
| 1491 | 0.300000 | 20 | 100 | gini | 0.879917 | 0.963953 |
| 1492 | 0.300000 | 20 | 100 | entropy | 0.873706 | 0.962088 |
| 1493 | 0.300000 | 20 | 100 | log_loss | 0.879917 | 0.963953 |
| 1494 | 0.300000 | 20 | 200 | gini | 0.881988 | 0.964574 |
| 1495 | 0.300000 | 20 | 200 | entropy | 0.877847 | 0.963331 |
| 1496 | 0.300000 | 20 | 200 | log_loss | 0.884058 | 0.965196 |
| 1497 | 0.300000 | 20 | 300 | gini | 0.886128 | 0.965817 |
| 1498 | 0.300000 | 20 | 300 | entropy | 0.879917 | 0.963953 |
| 1499 | 0.300000 | 20 | 300 | log_loss | 0.890269 | 0.967060 |
Row=264, Test_size=0.1, Max_depth=18, n_estimators=200, criterion=gini, Accuracy=0.925466, Score=0.992542
In [49]:
X_train, X_test, y_train, y_test = train_test_split(Xr, yr, test_size=0.1, random_state=0)
In [50]:
clf_rf = RandomForestClassifier(max_depth=19, n_estimators=200, criterion='gini')
clf_rf.fit(X_train, y_train)
y_pred = clf_rf.predict(X_test)
In [51]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_rf.score(Xr, yr))
Accuracy = 0.906832298136646 Score = 0.9906774394033562
SVM¶
In [52]:
Xs = df_train.drop('price_range', axis=1)
ys = df_train.price_range.values.reshape(-1, 1)
In [53]:
def svc(Xs, ys, Testsize):
df_evaluation_svm = pd.DataFrame()
C_values = [0.1, 1, 10]
kernel_values = ['linear', 'rbf', 'poly', 'sigmoid']
decision_function_shape_values = ['ovo', 'ovr']
degree_values = [2, 3, 4]
gamma_values = ['scale', 'auto']
for C in C_values:
for kernel in kernel_values:
for decision_function_shape in decision_function_shape_values:
for degree in degree_values:
for gamma in gamma_values:
for x in Testsize:
X_train, X_test, y_train, y_test = train_test_split(Xs, ys, test_size=x, random_state=0)
svm = SVC(C=C, kernel=kernel, decision_function_shape=decision_function_shape, degree=degree, gamma=gamma)
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
dict = {'C': C, 'kernel': kernel, 'decision_function_shape': decision_function_shape, 'degree': degree, 'gamma': gamma, 'Test_size': x, 'acc': metrics.accuracy_score(y_test, y_pred), 'score': svm.score(Xs, ys)}
df_evaluation_svm = pd.concat([df_evaluation_svm, pd.DataFrame(dict, index=[0])], ignore_index=True)
return (df_evaluation_svm)
df_evaluation_svm = svc(Xs, ys, [.1, .15, .2, .25, .3])
df_evaluation_svm.style.apply(highlight_max)
Out[53]:
| C | kernel | decision_function_shape | degree | gamma | Test_size | acc | score | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.100000 | linear | ovo | 2 | scale | 0.100000 | 0.981366 | 0.987570 |
| 1 | 0.100000 | linear | ovo | 2 | scale | 0.150000 | 0.983471 | 0.988813 |
| 2 | 0.100000 | linear | ovo | 2 | scale | 0.200000 | 0.965839 | 0.987570 |
| 3 | 0.100000 | linear | ovo | 2 | scale | 0.250000 | 0.965261 | 0.985705 |
| 4 | 0.100000 | linear | ovo | 2 | scale | 0.300000 | 0.964803 | 0.983841 |
| 5 | 0.100000 | linear | ovo | 2 | auto | 0.100000 | 0.981366 | 0.987570 |
| 6 | 0.100000 | linear | ovo | 2 | auto | 0.150000 | 0.983471 | 0.988813 |
| 7 | 0.100000 | linear | ovo | 2 | auto | 0.200000 | 0.965839 | 0.987570 |
| 8 | 0.100000 | linear | ovo | 2 | auto | 0.250000 | 0.965261 | 0.985705 |
| 9 | 0.100000 | linear | ovo | 2 | auto | 0.300000 | 0.964803 | 0.983841 |
| 10 | 0.100000 | linear | ovo | 3 | scale | 0.100000 | 0.981366 | 0.987570 |
| 11 | 0.100000 | linear | ovo | 3 | scale | 0.150000 | 0.983471 | 0.988813 |
| 12 | 0.100000 | linear | ovo | 3 | scale | 0.200000 | 0.965839 | 0.987570 |
| 13 | 0.100000 | linear | ovo | 3 | scale | 0.250000 | 0.965261 | 0.985705 |
| 14 | 0.100000 | linear | ovo | 3 | scale | 0.300000 | 0.964803 | 0.983841 |
| 15 | 0.100000 | linear | ovo | 3 | auto | 0.100000 | 0.981366 | 0.987570 |
| 16 | 0.100000 | linear | ovo | 3 | auto | 0.150000 | 0.983471 | 0.988813 |
| 17 | 0.100000 | linear | ovo | 3 | auto | 0.200000 | 0.965839 | 0.987570 |
| 18 | 0.100000 | linear | ovo | 3 | auto | 0.250000 | 0.965261 | 0.985705 |
| 19 | 0.100000 | linear | ovo | 3 | auto | 0.300000 | 0.964803 | 0.983841 |
| 20 | 0.100000 | linear | ovo | 4 | scale | 0.100000 | 0.981366 | 0.987570 |
| 21 | 0.100000 | linear | ovo | 4 | scale | 0.150000 | 0.983471 | 0.988813 |
| 22 | 0.100000 | linear | ovo | 4 | scale | 0.200000 | 0.965839 | 0.987570 |
| 23 | 0.100000 | linear | ovo | 4 | scale | 0.250000 | 0.965261 | 0.985705 |
| 24 | 0.100000 | linear | ovo | 4 | scale | 0.300000 | 0.964803 | 0.983841 |
| 25 | 0.100000 | linear | ovo | 4 | auto | 0.100000 | 0.981366 | 0.987570 |
| 26 | 0.100000 | linear | ovo | 4 | auto | 0.150000 | 0.983471 | 0.988813 |
| 27 | 0.100000 | linear | ovo | 4 | auto | 0.200000 | 0.965839 | 0.987570 |
| 28 | 0.100000 | linear | ovo | 4 | auto | 0.250000 | 0.965261 | 0.985705 |
| 29 | 0.100000 | linear | ovo | 4 | auto | 0.300000 | 0.964803 | 0.983841 |
| 30 | 0.100000 | linear | ovr | 2 | scale | 0.100000 | 0.981366 | 0.987570 |
| 31 | 0.100000 | linear | ovr | 2 | scale | 0.150000 | 0.983471 | 0.988813 |
| 32 | 0.100000 | linear | ovr | 2 | scale | 0.200000 | 0.965839 | 0.987570 |
| 33 | 0.100000 | linear | ovr | 2 | scale | 0.250000 | 0.965261 | 0.985705 |
| 34 | 0.100000 | linear | ovr | 2 | scale | 0.300000 | 0.964803 | 0.983841 |
| 35 | 0.100000 | linear | ovr | 2 | auto | 0.100000 | 0.981366 | 0.987570 |
| 36 | 0.100000 | linear | ovr | 2 | auto | 0.150000 | 0.983471 | 0.988813 |
| 37 | 0.100000 | linear | ovr | 2 | auto | 0.200000 | 0.965839 | 0.987570 |
| 38 | 0.100000 | linear | ovr | 2 | auto | 0.250000 | 0.965261 | 0.985705 |
| 39 | 0.100000 | linear | ovr | 2 | auto | 0.300000 | 0.964803 | 0.983841 |
| 40 | 0.100000 | linear | ovr | 3 | scale | 0.100000 | 0.981366 | 0.987570 |
| 41 | 0.100000 | linear | ovr | 3 | scale | 0.150000 | 0.983471 | 0.988813 |
| 42 | 0.100000 | linear | ovr | 3 | scale | 0.200000 | 0.965839 | 0.987570 |
| 43 | 0.100000 | linear | ovr | 3 | scale | 0.250000 | 0.965261 | 0.985705 |
| 44 | 0.100000 | linear | ovr | 3 | scale | 0.300000 | 0.964803 | 0.983841 |
| 45 | 0.100000 | linear | ovr | 3 | auto | 0.100000 | 0.981366 | 0.987570 |
| 46 | 0.100000 | linear | ovr | 3 | auto | 0.150000 | 0.983471 | 0.988813 |
| 47 | 0.100000 | linear | ovr | 3 | auto | 0.200000 | 0.965839 | 0.987570 |
| 48 | 0.100000 | linear | ovr | 3 | auto | 0.250000 | 0.965261 | 0.985705 |
| 49 | 0.100000 | linear | ovr | 3 | auto | 0.300000 | 0.964803 | 0.983841 |
| 50 | 0.100000 | linear | ovr | 4 | scale | 0.100000 | 0.981366 | 0.987570 |
| 51 | 0.100000 | linear | ovr | 4 | scale | 0.150000 | 0.983471 | 0.988813 |
| 52 | 0.100000 | linear | ovr | 4 | scale | 0.200000 | 0.965839 | 0.987570 |
| 53 | 0.100000 | linear | ovr | 4 | scale | 0.250000 | 0.965261 | 0.985705 |
| 54 | 0.100000 | linear | ovr | 4 | scale | 0.300000 | 0.964803 | 0.983841 |
| 55 | 0.100000 | linear | ovr | 4 | auto | 0.100000 | 0.981366 | 0.987570 |
| 56 | 0.100000 | linear | ovr | 4 | auto | 0.150000 | 0.983471 | 0.988813 |
| 57 | 0.100000 | linear | ovr | 4 | auto | 0.200000 | 0.965839 | 0.987570 |
| 58 | 0.100000 | linear | ovr | 4 | auto | 0.250000 | 0.965261 | 0.985705 |
| 59 | 0.100000 | linear | ovr | 4 | auto | 0.300000 | 0.964803 | 0.983841 |
| 60 | 0.100000 | rbf | ovo | 2 | scale | 0.100000 | 0.931677 | 0.899938 |
| 61 | 0.100000 | rbf | ovo | 2 | scale | 0.150000 | 0.913223 | 0.897452 |
| 62 | 0.100000 | rbf | ovo | 2 | scale | 0.200000 | 0.891304 | 0.893101 |
| 63 | 0.100000 | rbf | ovo | 2 | scale | 0.250000 | 0.883375 | 0.889372 |
| 64 | 0.100000 | rbf | ovo | 2 | scale | 0.300000 | 0.888199 | 0.891237 |
| 65 | 0.100000 | rbf | ovo | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 66 | 0.100000 | rbf | ovo | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 67 | 0.100000 | rbf | ovo | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 68 | 0.100000 | rbf | ovo | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 69 | 0.100000 | rbf | ovo | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 70 | 0.100000 | rbf | ovo | 3 | scale | 0.100000 | 0.931677 | 0.899938 |
| 71 | 0.100000 | rbf | ovo | 3 | scale | 0.150000 | 0.913223 | 0.897452 |
| 72 | 0.100000 | rbf | ovo | 3 | scale | 0.200000 | 0.891304 | 0.893101 |
| 73 | 0.100000 | rbf | ovo | 3 | scale | 0.250000 | 0.883375 | 0.889372 |
| 74 | 0.100000 | rbf | ovo | 3 | scale | 0.300000 | 0.888199 | 0.891237 |
| 75 | 0.100000 | rbf | ovo | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 76 | 0.100000 | rbf | ovo | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 77 | 0.100000 | rbf | ovo | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 78 | 0.100000 | rbf | ovo | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 79 | 0.100000 | rbf | ovo | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 80 | 0.100000 | rbf | ovo | 4 | scale | 0.100000 | 0.931677 | 0.899938 |
| 81 | 0.100000 | rbf | ovo | 4 | scale | 0.150000 | 0.913223 | 0.897452 |
| 82 | 0.100000 | rbf | ovo | 4 | scale | 0.200000 | 0.891304 | 0.893101 |
| 83 | 0.100000 | rbf | ovo | 4 | scale | 0.250000 | 0.883375 | 0.889372 |
| 84 | 0.100000 | rbf | ovo | 4 | scale | 0.300000 | 0.888199 | 0.891237 |
| 85 | 0.100000 | rbf | ovo | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 86 | 0.100000 | rbf | ovo | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 87 | 0.100000 | rbf | ovo | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 88 | 0.100000 | rbf | ovo | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 89 | 0.100000 | rbf | ovo | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 90 | 0.100000 | rbf | ovr | 2 | scale | 0.100000 | 0.931677 | 0.899938 |
| 91 | 0.100000 | rbf | ovr | 2 | scale | 0.150000 | 0.913223 | 0.897452 |
| 92 | 0.100000 | rbf | ovr | 2 | scale | 0.200000 | 0.891304 | 0.893101 |
| 93 | 0.100000 | rbf | ovr | 2 | scale | 0.250000 | 0.883375 | 0.889372 |
| 94 | 0.100000 | rbf | ovr | 2 | scale | 0.300000 | 0.888199 | 0.891237 |
| 95 | 0.100000 | rbf | ovr | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 96 | 0.100000 | rbf | ovr | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 97 | 0.100000 | rbf | ovr | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 98 | 0.100000 | rbf | ovr | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 99 | 0.100000 | rbf | ovr | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 100 | 0.100000 | rbf | ovr | 3 | scale | 0.100000 | 0.931677 | 0.899938 |
| 101 | 0.100000 | rbf | ovr | 3 | scale | 0.150000 | 0.913223 | 0.897452 |
| 102 | 0.100000 | rbf | ovr | 3 | scale | 0.200000 | 0.891304 | 0.893101 |
| 103 | 0.100000 | rbf | ovr | 3 | scale | 0.250000 | 0.883375 | 0.889372 |
| 104 | 0.100000 | rbf | ovr | 3 | scale | 0.300000 | 0.888199 | 0.891237 |
| 105 | 0.100000 | rbf | ovr | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 106 | 0.100000 | rbf | ovr | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 107 | 0.100000 | rbf | ovr | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 108 | 0.100000 | rbf | ovr | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 109 | 0.100000 | rbf | ovr | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 110 | 0.100000 | rbf | ovr | 4 | scale | 0.100000 | 0.931677 | 0.899938 |
| 111 | 0.100000 | rbf | ovr | 4 | scale | 0.150000 | 0.913223 | 0.897452 |
| 112 | 0.100000 | rbf | ovr | 4 | scale | 0.200000 | 0.891304 | 0.893101 |
| 113 | 0.100000 | rbf | ovr | 4 | scale | 0.250000 | 0.883375 | 0.889372 |
| 114 | 0.100000 | rbf | ovr | 4 | scale | 0.300000 | 0.888199 | 0.891237 |
| 115 | 0.100000 | rbf | ovr | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 116 | 0.100000 | rbf | ovr | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 117 | 0.100000 | rbf | ovr | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 118 | 0.100000 | rbf | ovr | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 119 | 0.100000 | rbf | ovr | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 120 | 0.100000 | poly | ovo | 2 | scale | 0.100000 | 0.950311 | 0.934121 |
| 121 | 0.100000 | poly | ovo | 2 | scale | 0.150000 | 0.942149 | 0.933499 |
| 122 | 0.100000 | poly | ovo | 2 | scale | 0.200000 | 0.931677 | 0.930392 |
| 123 | 0.100000 | poly | ovo | 2 | scale | 0.250000 | 0.915633 | 0.929149 |
| 124 | 0.100000 | poly | ovo | 2 | scale | 0.300000 | 0.913043 | 0.920447 |
| 125 | 0.100000 | poly | ovo | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 126 | 0.100000 | poly | ovo | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 127 | 0.100000 | poly | ovo | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 128 | 0.100000 | poly | ovo | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 129 | 0.100000 | poly | ovo | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 130 | 0.100000 | poly | ovo | 3 | scale | 0.100000 | 0.944099 | 0.934121 |
| 131 | 0.100000 | poly | ovo | 3 | scale | 0.150000 | 0.946281 | 0.932878 |
| 132 | 0.100000 | poly | ovo | 3 | scale | 0.200000 | 0.937888 | 0.933499 |
| 133 | 0.100000 | poly | ovo | 3 | scale | 0.250000 | 0.928040 | 0.928527 |
| 134 | 0.100000 | poly | ovo | 3 | scale | 0.300000 | 0.923395 | 0.922933 |
| 135 | 0.100000 | poly | ovo | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 136 | 0.100000 | poly | ovo | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 137 | 0.100000 | poly | ovo | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 138 | 0.100000 | poly | ovo | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 139 | 0.100000 | poly | ovo | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 140 | 0.100000 | poly | ovo | 4 | scale | 0.100000 | 0.937888 | 0.933499 |
| 141 | 0.100000 | poly | ovo | 4 | scale | 0.150000 | 0.946281 | 0.932878 |
| 142 | 0.100000 | poly | ovo | 4 | scale | 0.200000 | 0.947205 | 0.932256 |
| 143 | 0.100000 | poly | ovo | 4 | scale | 0.250000 | 0.940447 | 0.931635 |
| 144 | 0.100000 | poly | ovo | 4 | scale | 0.300000 | 0.933747 | 0.926663 |
| 145 | 0.100000 | poly | ovo | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 146 | 0.100000 | poly | ovo | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 147 | 0.100000 | poly | ovo | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 148 | 0.100000 | poly | ovo | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 149 | 0.100000 | poly | ovo | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 150 | 0.100000 | poly | ovr | 2 | scale | 0.100000 | 0.950311 | 0.934121 |
| 151 | 0.100000 | poly | ovr | 2 | scale | 0.150000 | 0.942149 | 0.933499 |
| 152 | 0.100000 | poly | ovr | 2 | scale | 0.200000 | 0.931677 | 0.930392 |
| 153 | 0.100000 | poly | ovr | 2 | scale | 0.250000 | 0.915633 | 0.929149 |
| 154 | 0.100000 | poly | ovr | 2 | scale | 0.300000 | 0.913043 | 0.920447 |
| 155 | 0.100000 | poly | ovr | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 156 | 0.100000 | poly | ovr | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 157 | 0.100000 | poly | ovr | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 158 | 0.100000 | poly | ovr | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 159 | 0.100000 | poly | ovr | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 160 | 0.100000 | poly | ovr | 3 | scale | 0.100000 | 0.944099 | 0.934121 |
| 161 | 0.100000 | poly | ovr | 3 | scale | 0.150000 | 0.946281 | 0.932878 |
| 162 | 0.100000 | poly | ovr | 3 | scale | 0.200000 | 0.937888 | 0.933499 |
| 163 | 0.100000 | poly | ovr | 3 | scale | 0.250000 | 0.928040 | 0.928527 |
| 164 | 0.100000 | poly | ovr | 3 | scale | 0.300000 | 0.923395 | 0.922933 |
| 165 | 0.100000 | poly | ovr | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 166 | 0.100000 | poly | ovr | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 167 | 0.100000 | poly | ovr | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 168 | 0.100000 | poly | ovr | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 169 | 0.100000 | poly | ovr | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 170 | 0.100000 | poly | ovr | 4 | scale | 0.100000 | 0.937888 | 0.933499 |
| 171 | 0.100000 | poly | ovr | 4 | scale | 0.150000 | 0.946281 | 0.932878 |
| 172 | 0.100000 | poly | ovr | 4 | scale | 0.200000 | 0.947205 | 0.932256 |
| 173 | 0.100000 | poly | ovr | 4 | scale | 0.250000 | 0.940447 | 0.931635 |
| 174 | 0.100000 | poly | ovr | 4 | scale | 0.300000 | 0.933747 | 0.926663 |
| 175 | 0.100000 | poly | ovr | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 176 | 0.100000 | poly | ovr | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 177 | 0.100000 | poly | ovr | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 178 | 0.100000 | poly | ovr | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 179 | 0.100000 | poly | ovr | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 180 | 0.100000 | sigmoid | ovo | 2 | scale | 0.100000 | 0.409938 | 0.423866 |
| 181 | 0.100000 | sigmoid | ovo | 2 | scale | 0.150000 | 0.429752 | 0.442511 |
| 182 | 0.100000 | sigmoid | ovo | 2 | scale | 0.200000 | 0.440994 | 0.444997 |
| 183 | 0.100000 | sigmoid | ovo | 2 | scale | 0.250000 | 0.233251 | 0.254195 |
| 184 | 0.100000 | sigmoid | ovo | 2 | scale | 0.300000 | 0.229814 | 0.240522 |
| 185 | 0.100000 | sigmoid | ovo | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 186 | 0.100000 | sigmoid | ovo | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 187 | 0.100000 | sigmoid | ovo | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 188 | 0.100000 | sigmoid | ovo | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 189 | 0.100000 | sigmoid | ovo | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 190 | 0.100000 | sigmoid | ovo | 3 | scale | 0.100000 | 0.409938 | 0.423866 |
| 191 | 0.100000 | sigmoid | ovo | 3 | scale | 0.150000 | 0.429752 | 0.442511 |
| 192 | 0.100000 | sigmoid | ovo | 3 | scale | 0.200000 | 0.440994 | 0.444997 |
| 193 | 0.100000 | sigmoid | ovo | 3 | scale | 0.250000 | 0.233251 | 0.254195 |
| 194 | 0.100000 | sigmoid | ovo | 3 | scale | 0.300000 | 0.229814 | 0.240522 |
| 195 | 0.100000 | sigmoid | ovo | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 196 | 0.100000 | sigmoid | ovo | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 197 | 0.100000 | sigmoid | ovo | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 198 | 0.100000 | sigmoid | ovo | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 199 | 0.100000 | sigmoid | ovo | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 200 | 0.100000 | sigmoid | ovo | 4 | scale | 0.100000 | 0.409938 | 0.423866 |
| 201 | 0.100000 | sigmoid | ovo | 4 | scale | 0.150000 | 0.429752 | 0.442511 |
| 202 | 0.100000 | sigmoid | ovo | 4 | scale | 0.200000 | 0.440994 | 0.444997 |
| 203 | 0.100000 | sigmoid | ovo | 4 | scale | 0.250000 | 0.233251 | 0.254195 |
| 204 | 0.100000 | sigmoid | ovo | 4 | scale | 0.300000 | 0.229814 | 0.240522 |
| 205 | 0.100000 | sigmoid | ovo | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 206 | 0.100000 | sigmoid | ovo | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 207 | 0.100000 | sigmoid | ovo | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 208 | 0.100000 | sigmoid | ovo | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 209 | 0.100000 | sigmoid | ovo | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 210 | 0.100000 | sigmoid | ovr | 2 | scale | 0.100000 | 0.409938 | 0.423866 |
| 211 | 0.100000 | sigmoid | ovr | 2 | scale | 0.150000 | 0.429752 | 0.442511 |
| 212 | 0.100000 | sigmoid | ovr | 2 | scale | 0.200000 | 0.440994 | 0.444997 |
| 213 | 0.100000 | sigmoid | ovr | 2 | scale | 0.250000 | 0.233251 | 0.254195 |
| 214 | 0.100000 | sigmoid | ovr | 2 | scale | 0.300000 | 0.229814 | 0.240522 |
| 215 | 0.100000 | sigmoid | ovr | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 216 | 0.100000 | sigmoid | ovr | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 217 | 0.100000 | sigmoid | ovr | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 218 | 0.100000 | sigmoid | ovr | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 219 | 0.100000 | sigmoid | ovr | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 220 | 0.100000 | sigmoid | ovr | 3 | scale | 0.100000 | 0.409938 | 0.423866 |
| 221 | 0.100000 | sigmoid | ovr | 3 | scale | 0.150000 | 0.429752 | 0.442511 |
| 222 | 0.100000 | sigmoid | ovr | 3 | scale | 0.200000 | 0.440994 | 0.444997 |
| 223 | 0.100000 | sigmoid | ovr | 3 | scale | 0.250000 | 0.233251 | 0.254195 |
| 224 | 0.100000 | sigmoid | ovr | 3 | scale | 0.300000 | 0.229814 | 0.240522 |
| 225 | 0.100000 | sigmoid | ovr | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 226 | 0.100000 | sigmoid | ovr | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 227 | 0.100000 | sigmoid | ovr | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 228 | 0.100000 | sigmoid | ovr | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 229 | 0.100000 | sigmoid | ovr | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 230 | 0.100000 | sigmoid | ovr | 4 | scale | 0.100000 | 0.409938 | 0.423866 |
| 231 | 0.100000 | sigmoid | ovr | 4 | scale | 0.150000 | 0.429752 | 0.442511 |
| 232 | 0.100000 | sigmoid | ovr | 4 | scale | 0.200000 | 0.440994 | 0.444997 |
| 233 | 0.100000 | sigmoid | ovr | 4 | scale | 0.250000 | 0.233251 | 0.254195 |
| 234 | 0.100000 | sigmoid | ovr | 4 | scale | 0.300000 | 0.229814 | 0.240522 |
| 235 | 0.100000 | sigmoid | ovr | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 236 | 0.100000 | sigmoid | ovr | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 237 | 0.100000 | sigmoid | ovr | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 238 | 0.100000 | sigmoid | ovr | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 239 | 0.100000 | sigmoid | ovr | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 240 | 1.000000 | linear | ovo | 2 | scale | 0.100000 | 0.968944 | 0.989434 |
| 241 | 1.000000 | linear | ovo | 2 | scale | 0.150000 | 0.975207 | 0.989434 |
| 242 | 1.000000 | linear | ovo | 2 | scale | 0.200000 | 0.965839 | 0.990056 |
| 243 | 1.000000 | linear | ovo | 2 | scale | 0.250000 | 0.965261 | 0.989434 |
| 244 | 1.000000 | linear | ovo | 2 | scale | 0.300000 | 0.968944 | 0.988813 |
| 245 | 1.000000 | linear | ovo | 2 | auto | 0.100000 | 0.968944 | 0.989434 |
| 246 | 1.000000 | linear | ovo | 2 | auto | 0.150000 | 0.975207 | 0.989434 |
| 247 | 1.000000 | linear | ovo | 2 | auto | 0.200000 | 0.965839 | 0.990056 |
| 248 | 1.000000 | linear | ovo | 2 | auto | 0.250000 | 0.965261 | 0.989434 |
| 249 | 1.000000 | linear | ovo | 2 | auto | 0.300000 | 0.968944 | 0.988813 |
| 250 | 1.000000 | linear | ovo | 3 | scale | 0.100000 | 0.968944 | 0.989434 |
| 251 | 1.000000 | linear | ovo | 3 | scale | 0.150000 | 0.975207 | 0.989434 |
| 252 | 1.000000 | linear | ovo | 3 | scale | 0.200000 | 0.965839 | 0.990056 |
| 253 | 1.000000 | linear | ovo | 3 | scale | 0.250000 | 0.965261 | 0.989434 |
| 254 | 1.000000 | linear | ovo | 3 | scale | 0.300000 | 0.968944 | 0.988813 |
| 255 | 1.000000 | linear | ovo | 3 | auto | 0.100000 | 0.968944 | 0.989434 |
| 256 | 1.000000 | linear | ovo | 3 | auto | 0.150000 | 0.975207 | 0.989434 |
| 257 | 1.000000 | linear | ovo | 3 | auto | 0.200000 | 0.965839 | 0.990056 |
| 258 | 1.000000 | linear | ovo | 3 | auto | 0.250000 | 0.965261 | 0.989434 |
| 259 | 1.000000 | linear | ovo | 3 | auto | 0.300000 | 0.968944 | 0.988813 |
| 260 | 1.000000 | linear | ovo | 4 | scale | 0.100000 | 0.968944 | 0.989434 |
| 261 | 1.000000 | linear | ovo | 4 | scale | 0.150000 | 0.975207 | 0.989434 |
| 262 | 1.000000 | linear | ovo | 4 | scale | 0.200000 | 0.965839 | 0.990056 |
| 263 | 1.000000 | linear | ovo | 4 | scale | 0.250000 | 0.965261 | 0.989434 |
| 264 | 1.000000 | linear | ovo | 4 | scale | 0.300000 | 0.968944 | 0.988813 |
| 265 | 1.000000 | linear | ovo | 4 | auto | 0.100000 | 0.968944 | 0.989434 |
| 266 | 1.000000 | linear | ovo | 4 | auto | 0.150000 | 0.975207 | 0.989434 |
| 267 | 1.000000 | linear | ovo | 4 | auto | 0.200000 | 0.965839 | 0.990056 |
| 268 | 1.000000 | linear | ovo | 4 | auto | 0.250000 | 0.965261 | 0.989434 |
| 269 | 1.000000 | linear | ovo | 4 | auto | 0.300000 | 0.968944 | 0.988813 |
| 270 | 1.000000 | linear | ovr | 2 | scale | 0.100000 | 0.968944 | 0.989434 |
| 271 | 1.000000 | linear | ovr | 2 | scale | 0.150000 | 0.975207 | 0.989434 |
| 272 | 1.000000 | linear | ovr | 2 | scale | 0.200000 | 0.965839 | 0.990056 |
| 273 | 1.000000 | linear | ovr | 2 | scale | 0.250000 | 0.965261 | 0.989434 |
| 274 | 1.000000 | linear | ovr | 2 | scale | 0.300000 | 0.968944 | 0.988813 |
| 275 | 1.000000 | linear | ovr | 2 | auto | 0.100000 | 0.968944 | 0.989434 |
| 276 | 1.000000 | linear | ovr | 2 | auto | 0.150000 | 0.975207 | 0.989434 |
| 277 | 1.000000 | linear | ovr | 2 | auto | 0.200000 | 0.965839 | 0.990056 |
| 278 | 1.000000 | linear | ovr | 2 | auto | 0.250000 | 0.965261 | 0.989434 |
| 279 | 1.000000 | linear | ovr | 2 | auto | 0.300000 | 0.968944 | 0.988813 |
| 280 | 1.000000 | linear | ovr | 3 | scale | 0.100000 | 0.968944 | 0.989434 |
| 281 | 1.000000 | linear | ovr | 3 | scale | 0.150000 | 0.975207 | 0.989434 |
| 282 | 1.000000 | linear | ovr | 3 | scale | 0.200000 | 0.965839 | 0.990056 |
| 283 | 1.000000 | linear | ovr | 3 | scale | 0.250000 | 0.965261 | 0.989434 |
| 284 | 1.000000 | linear | ovr | 3 | scale | 0.300000 | 0.968944 | 0.988813 |
| 285 | 1.000000 | linear | ovr | 3 | auto | 0.100000 | 0.968944 | 0.989434 |
| 286 | 1.000000 | linear | ovr | 3 | auto | 0.150000 | 0.975207 | 0.989434 |
| 287 | 1.000000 | linear | ovr | 3 | auto | 0.200000 | 0.965839 | 0.990056 |
| 288 | 1.000000 | linear | ovr | 3 | auto | 0.250000 | 0.965261 | 0.989434 |
| 289 | 1.000000 | linear | ovr | 3 | auto | 0.300000 | 0.968944 | 0.988813 |
| 290 | 1.000000 | linear | ovr | 4 | scale | 0.100000 | 0.968944 | 0.989434 |
| 291 | 1.000000 | linear | ovr | 4 | scale | 0.150000 | 0.975207 | 0.989434 |
| 292 | 1.000000 | linear | ovr | 4 | scale | 0.200000 | 0.965839 | 0.990056 |
| 293 | 1.000000 | linear | ovr | 4 | scale | 0.250000 | 0.965261 | 0.989434 |
| 294 | 1.000000 | linear | ovr | 4 | scale | 0.300000 | 0.968944 | 0.988813 |
| 295 | 1.000000 | linear | ovr | 4 | auto | 0.100000 | 0.968944 | 0.989434 |
| 296 | 1.000000 | linear | ovr | 4 | auto | 0.150000 | 0.975207 | 0.989434 |
| 297 | 1.000000 | linear | ovr | 4 | auto | 0.200000 | 0.965839 | 0.990056 |
| 298 | 1.000000 | linear | ovr | 4 | auto | 0.250000 | 0.965261 | 0.989434 |
| 299 | 1.000000 | linear | ovr | 4 | auto | 0.300000 | 0.968944 | 0.988813 |
| 300 | 1.000000 | rbf | ovo | 2 | scale | 0.100000 | 0.956522 | 0.954630 |
| 301 | 1.000000 | rbf | ovo | 2 | scale | 0.150000 | 0.958678 | 0.955873 |
| 302 | 1.000000 | rbf | ovo | 2 | scale | 0.200000 | 0.959627 | 0.955873 |
| 303 | 1.000000 | rbf | ovo | 2 | scale | 0.250000 | 0.952854 | 0.952766 |
| 304 | 1.000000 | rbf | ovo | 2 | scale | 0.300000 | 0.962733 | 0.954009 |
| 305 | 1.000000 | rbf | ovo | 2 | auto | 0.100000 | 0.242236 | 0.924177 |
| 306 | 1.000000 | rbf | ovo | 2 | auto | 0.150000 | 0.252066 | 0.887508 |
| 307 | 1.000000 | rbf | ovo | 2 | auto | 0.200000 | 0.214286 | 0.842759 |
| 308 | 1.000000 | rbf | ovo | 2 | auto | 0.250000 | 0.210918 | 0.802362 |
| 309 | 1.000000 | rbf | ovo | 2 | auto | 0.300000 | 0.229814 | 0.768800 |
| 310 | 1.000000 | rbf | ovo | 3 | scale | 0.100000 | 0.956522 | 0.954630 |
| 311 | 1.000000 | rbf | ovo | 3 | scale | 0.150000 | 0.958678 | 0.955873 |
| 312 | 1.000000 | rbf | ovo | 3 | scale | 0.200000 | 0.959627 | 0.955873 |
| 313 | 1.000000 | rbf | ovo | 3 | scale | 0.250000 | 0.952854 | 0.952766 |
| 314 | 1.000000 | rbf | ovo | 3 | scale | 0.300000 | 0.962733 | 0.954009 |
| 315 | 1.000000 | rbf | ovo | 3 | auto | 0.100000 | 0.242236 | 0.924177 |
| 316 | 1.000000 | rbf | ovo | 3 | auto | 0.150000 | 0.252066 | 0.887508 |
| 317 | 1.000000 | rbf | ovo | 3 | auto | 0.200000 | 0.214286 | 0.842759 |
| 318 | 1.000000 | rbf | ovo | 3 | auto | 0.250000 | 0.210918 | 0.802362 |
| 319 | 1.000000 | rbf | ovo | 3 | auto | 0.300000 | 0.229814 | 0.768800 |
| 320 | 1.000000 | rbf | ovo | 4 | scale | 0.100000 | 0.956522 | 0.954630 |
| 321 | 1.000000 | rbf | ovo | 4 | scale | 0.150000 | 0.958678 | 0.955873 |
| 322 | 1.000000 | rbf | ovo | 4 | scale | 0.200000 | 0.959627 | 0.955873 |
| 323 | 1.000000 | rbf | ovo | 4 | scale | 0.250000 | 0.952854 | 0.952766 |
| 324 | 1.000000 | rbf | ovo | 4 | scale | 0.300000 | 0.962733 | 0.954009 |
| 325 | 1.000000 | rbf | ovo | 4 | auto | 0.100000 | 0.242236 | 0.924177 |
| 326 | 1.000000 | rbf | ovo | 4 | auto | 0.150000 | 0.252066 | 0.887508 |
| 327 | 1.000000 | rbf | ovo | 4 | auto | 0.200000 | 0.214286 | 0.842759 |
| 328 | 1.000000 | rbf | ovo | 4 | auto | 0.250000 | 0.210918 | 0.802362 |
| 329 | 1.000000 | rbf | ovo | 4 | auto | 0.300000 | 0.229814 | 0.768800 |
| 330 | 1.000000 | rbf | ovr | 2 | scale | 0.100000 | 0.956522 | 0.954630 |
| 331 | 1.000000 | rbf | ovr | 2 | scale | 0.150000 | 0.958678 | 0.955873 |
| 332 | 1.000000 | rbf | ovr | 2 | scale | 0.200000 | 0.959627 | 0.955873 |
| 333 | 1.000000 | rbf | ovr | 2 | scale | 0.250000 | 0.952854 | 0.952766 |
| 334 | 1.000000 | rbf | ovr | 2 | scale | 0.300000 | 0.962733 | 0.954009 |
| 335 | 1.000000 | rbf | ovr | 2 | auto | 0.100000 | 0.242236 | 0.924177 |
| 336 | 1.000000 | rbf | ovr | 2 | auto | 0.150000 | 0.252066 | 0.887508 |
| 337 | 1.000000 | rbf | ovr | 2 | auto | 0.200000 | 0.214286 | 0.842759 |
| 338 | 1.000000 | rbf | ovr | 2 | auto | 0.250000 | 0.210918 | 0.802362 |
| 339 | 1.000000 | rbf | ovr | 2 | auto | 0.300000 | 0.229814 | 0.768800 |
| 340 | 1.000000 | rbf | ovr | 3 | scale | 0.100000 | 0.956522 | 0.954630 |
| 341 | 1.000000 | rbf | ovr | 3 | scale | 0.150000 | 0.958678 | 0.955873 |
| 342 | 1.000000 | rbf | ovr | 3 | scale | 0.200000 | 0.959627 | 0.955873 |
| 343 | 1.000000 | rbf | ovr | 3 | scale | 0.250000 | 0.952854 | 0.952766 |
| 344 | 1.000000 | rbf | ovr | 3 | scale | 0.300000 | 0.962733 | 0.954009 |
| 345 | 1.000000 | rbf | ovr | 3 | auto | 0.100000 | 0.242236 | 0.924177 |
| 346 | 1.000000 | rbf | ovr | 3 | auto | 0.150000 | 0.252066 | 0.887508 |
| 347 | 1.000000 | rbf | ovr | 3 | auto | 0.200000 | 0.214286 | 0.842759 |
| 348 | 1.000000 | rbf | ovr | 3 | auto | 0.250000 | 0.210918 | 0.802362 |
| 349 | 1.000000 | rbf | ovr | 3 | auto | 0.300000 | 0.229814 | 0.768800 |
| 350 | 1.000000 | rbf | ovr | 4 | scale | 0.100000 | 0.956522 | 0.954630 |
| 351 | 1.000000 | rbf | ovr | 4 | scale | 0.150000 | 0.958678 | 0.955873 |
| 352 | 1.000000 | rbf | ovr | 4 | scale | 0.200000 | 0.959627 | 0.955873 |
| 353 | 1.000000 | rbf | ovr | 4 | scale | 0.250000 | 0.952854 | 0.952766 |
| 354 | 1.000000 | rbf | ovr | 4 | scale | 0.300000 | 0.962733 | 0.954009 |
| 355 | 1.000000 | rbf | ovr | 4 | auto | 0.100000 | 0.242236 | 0.924177 |
| 356 | 1.000000 | rbf | ovr | 4 | auto | 0.150000 | 0.252066 | 0.887508 |
| 357 | 1.000000 | rbf | ovr | 4 | auto | 0.200000 | 0.214286 | 0.842759 |
| 358 | 1.000000 | rbf | ovr | 4 | auto | 0.250000 | 0.210918 | 0.802362 |
| 359 | 1.000000 | rbf | ovr | 4 | auto | 0.300000 | 0.229814 | 0.768800 |
| 360 | 1.000000 | poly | ovo | 2 | scale | 0.100000 | 0.956522 | 0.956495 |
| 361 | 1.000000 | poly | ovo | 2 | scale | 0.150000 | 0.966942 | 0.957738 |
| 362 | 1.000000 | poly | ovo | 2 | scale | 0.200000 | 0.959627 | 0.958981 |
| 363 | 1.000000 | poly | ovo | 2 | scale | 0.250000 | 0.957816 | 0.960224 |
| 364 | 1.000000 | poly | ovo | 2 | scale | 0.300000 | 0.958592 | 0.957116 |
| 365 | 1.000000 | poly | ovo | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 366 | 1.000000 | poly | ovo | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 367 | 1.000000 | poly | ovo | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 368 | 1.000000 | poly | ovo | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 369 | 1.000000 | poly | ovo | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 370 | 1.000000 | poly | ovo | 3 | scale | 0.100000 | 0.962733 | 0.954630 |
| 371 | 1.000000 | poly | ovo | 3 | scale | 0.150000 | 0.966942 | 0.956495 |
| 372 | 1.000000 | poly | ovo | 3 | scale | 0.200000 | 0.953416 | 0.956495 |
| 373 | 1.000000 | poly | ovo | 3 | scale | 0.250000 | 0.950372 | 0.958981 |
| 374 | 1.000000 | poly | ovo | 3 | scale | 0.300000 | 0.962733 | 0.957738 |
| 375 | 1.000000 | poly | ovo | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 376 | 1.000000 | poly | ovo | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 377 | 1.000000 | poly | ovo | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 378 | 1.000000 | poly | ovo | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 379 | 1.000000 | poly | ovo | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 380 | 1.000000 | poly | ovo | 4 | scale | 0.100000 | 0.962733 | 0.954630 |
| 381 | 1.000000 | poly | ovo | 4 | scale | 0.150000 | 0.962810 | 0.952766 |
| 382 | 1.000000 | poly | ovo | 4 | scale | 0.200000 | 0.965839 | 0.955873 |
| 383 | 1.000000 | poly | ovo | 4 | scale | 0.250000 | 0.955335 | 0.956495 |
| 384 | 1.000000 | poly | ovo | 4 | scale | 0.300000 | 0.958592 | 0.955252 |
| 385 | 1.000000 | poly | ovo | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 386 | 1.000000 | poly | ovo | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 387 | 1.000000 | poly | ovo | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 388 | 1.000000 | poly | ovo | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 389 | 1.000000 | poly | ovo | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 390 | 1.000000 | poly | ovr | 2 | scale | 0.100000 | 0.956522 | 0.956495 |
| 391 | 1.000000 | poly | ovr | 2 | scale | 0.150000 | 0.966942 | 0.957738 |
| 392 | 1.000000 | poly | ovr | 2 | scale | 0.200000 | 0.959627 | 0.958981 |
| 393 | 1.000000 | poly | ovr | 2 | scale | 0.250000 | 0.957816 | 0.960224 |
| 394 | 1.000000 | poly | ovr | 2 | scale | 0.300000 | 0.958592 | 0.957116 |
| 395 | 1.000000 | poly | ovr | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 396 | 1.000000 | poly | ovr | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 397 | 1.000000 | poly | ovr | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 398 | 1.000000 | poly | ovr | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 399 | 1.000000 | poly | ovr | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 400 | 1.000000 | poly | ovr | 3 | scale | 0.100000 | 0.962733 | 0.954630 |
| 401 | 1.000000 | poly | ovr | 3 | scale | 0.150000 | 0.966942 | 0.956495 |
| 402 | 1.000000 | poly | ovr | 3 | scale | 0.200000 | 0.953416 | 0.956495 |
| 403 | 1.000000 | poly | ovr | 3 | scale | 0.250000 | 0.950372 | 0.958981 |
| 404 | 1.000000 | poly | ovr | 3 | scale | 0.300000 | 0.962733 | 0.957738 |
| 405 | 1.000000 | poly | ovr | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 406 | 1.000000 | poly | ovr | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 407 | 1.000000 | poly | ovr | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 408 | 1.000000 | poly | ovr | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 409 | 1.000000 | poly | ovr | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 410 | 1.000000 | poly | ovr | 4 | scale | 0.100000 | 0.962733 | 0.954630 |
| 411 | 1.000000 | poly | ovr | 4 | scale | 0.150000 | 0.962810 | 0.952766 |
| 412 | 1.000000 | poly | ovr | 4 | scale | 0.200000 | 0.965839 | 0.955873 |
| 413 | 1.000000 | poly | ovr | 4 | scale | 0.250000 | 0.955335 | 0.956495 |
| 414 | 1.000000 | poly | ovr | 4 | scale | 0.300000 | 0.958592 | 0.955252 |
| 415 | 1.000000 | poly | ovr | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 416 | 1.000000 | poly | ovr | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 417 | 1.000000 | poly | ovr | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 418 | 1.000000 | poly | ovr | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 419 | 1.000000 | poly | ovr | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 420 | 1.000000 | sigmoid | ovo | 2 | scale | 0.100000 | 0.192547 | 0.185208 |
| 421 | 1.000000 | sigmoid | ovo | 2 | scale | 0.150000 | 0.185950 | 0.188937 |
| 422 | 1.000000 | sigmoid | ovo | 2 | scale | 0.200000 | 0.183230 | 0.197638 |
| 423 | 1.000000 | sigmoid | ovo | 2 | scale | 0.250000 | 0.183623 | 0.201367 |
| 424 | 1.000000 | sigmoid | ovo | 2 | scale | 0.300000 | 0.190476 | 0.202610 |
| 425 | 1.000000 | sigmoid | ovo | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 426 | 1.000000 | sigmoid | ovo | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 427 | 1.000000 | sigmoid | ovo | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 428 | 1.000000 | sigmoid | ovo | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 429 | 1.000000 | sigmoid | ovo | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 430 | 1.000000 | sigmoid | ovo | 3 | scale | 0.100000 | 0.192547 | 0.185208 |
| 431 | 1.000000 | sigmoid | ovo | 3 | scale | 0.150000 | 0.185950 | 0.188937 |
| 432 | 1.000000 | sigmoid | ovo | 3 | scale | 0.200000 | 0.183230 | 0.197638 |
| 433 | 1.000000 | sigmoid | ovo | 3 | scale | 0.250000 | 0.183623 | 0.201367 |
| 434 | 1.000000 | sigmoid | ovo | 3 | scale | 0.300000 | 0.190476 | 0.202610 |
| 435 | 1.000000 | sigmoid | ovo | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 436 | 1.000000 | sigmoid | ovo | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 437 | 1.000000 | sigmoid | ovo | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 438 | 1.000000 | sigmoid | ovo | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 439 | 1.000000 | sigmoid | ovo | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 440 | 1.000000 | sigmoid | ovo | 4 | scale | 0.100000 | 0.192547 | 0.185208 |
| 441 | 1.000000 | sigmoid | ovo | 4 | scale | 0.150000 | 0.185950 | 0.188937 |
| 442 | 1.000000 | sigmoid | ovo | 4 | scale | 0.200000 | 0.183230 | 0.197638 |
| 443 | 1.000000 | sigmoid | ovo | 4 | scale | 0.250000 | 0.183623 | 0.201367 |
| 444 | 1.000000 | sigmoid | ovo | 4 | scale | 0.300000 | 0.190476 | 0.202610 |
| 445 | 1.000000 | sigmoid | ovo | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 446 | 1.000000 | sigmoid | ovo | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 447 | 1.000000 | sigmoid | ovo | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 448 | 1.000000 | sigmoid | ovo | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 449 | 1.000000 | sigmoid | ovo | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 450 | 1.000000 | sigmoid | ovr | 2 | scale | 0.100000 | 0.192547 | 0.185208 |
| 451 | 1.000000 | sigmoid | ovr | 2 | scale | 0.150000 | 0.185950 | 0.188937 |
| 452 | 1.000000 | sigmoid | ovr | 2 | scale | 0.200000 | 0.183230 | 0.197638 |
| 453 | 1.000000 | sigmoid | ovr | 2 | scale | 0.250000 | 0.183623 | 0.201367 |
| 454 | 1.000000 | sigmoid | ovr | 2 | scale | 0.300000 | 0.190476 | 0.202610 |
| 455 | 1.000000 | sigmoid | ovr | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 456 | 1.000000 | sigmoid | ovr | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 457 | 1.000000 | sigmoid | ovr | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 458 | 1.000000 | sigmoid | ovr | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 459 | 1.000000 | sigmoid | ovr | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 460 | 1.000000 | sigmoid | ovr | 3 | scale | 0.100000 | 0.192547 | 0.185208 |
| 461 | 1.000000 | sigmoid | ovr | 3 | scale | 0.150000 | 0.185950 | 0.188937 |
| 462 | 1.000000 | sigmoid | ovr | 3 | scale | 0.200000 | 0.183230 | 0.197638 |
| 463 | 1.000000 | sigmoid | ovr | 3 | scale | 0.250000 | 0.183623 | 0.201367 |
| 464 | 1.000000 | sigmoid | ovr | 3 | scale | 0.300000 | 0.190476 | 0.202610 |
| 465 | 1.000000 | sigmoid | ovr | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 466 | 1.000000 | sigmoid | ovr | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 467 | 1.000000 | sigmoid | ovr | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 468 | 1.000000 | sigmoid | ovr | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 469 | 1.000000 | sigmoid | ovr | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 470 | 1.000000 | sigmoid | ovr | 4 | scale | 0.100000 | 0.192547 | 0.185208 |
| 471 | 1.000000 | sigmoid | ovr | 4 | scale | 0.150000 | 0.185950 | 0.188937 |
| 472 | 1.000000 | sigmoid | ovr | 4 | scale | 0.200000 | 0.183230 | 0.197638 |
| 473 | 1.000000 | sigmoid | ovr | 4 | scale | 0.250000 | 0.183623 | 0.201367 |
| 474 | 1.000000 | sigmoid | ovr | 4 | scale | 0.300000 | 0.190476 | 0.202610 |
| 475 | 1.000000 | sigmoid | ovr | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 476 | 1.000000 | sigmoid | ovr | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 477 | 1.000000 | sigmoid | ovr | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 478 | 1.000000 | sigmoid | ovr | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 479 | 1.000000 | sigmoid | ovr | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 480 | 10.000000 | linear | ovo | 2 | scale | 0.100000 | 0.968944 | 0.984462 |
| 481 | 10.000000 | linear | ovo | 2 | scale | 0.150000 | 0.975207 | 0.986327 |
| 482 | 10.000000 | linear | ovo | 2 | scale | 0.200000 | 0.962733 | 0.988191 |
| 483 | 10.000000 | linear | ovo | 2 | scale | 0.250000 | 0.962779 | 0.988813 |
| 484 | 10.000000 | linear | ovo | 2 | scale | 0.300000 | 0.966874 | 0.988191 |
| 485 | 10.000000 | linear | ovo | 2 | auto | 0.100000 | 0.968944 | 0.984462 |
| 486 | 10.000000 | linear | ovo | 2 | auto | 0.150000 | 0.975207 | 0.986327 |
| 487 | 10.000000 | linear | ovo | 2 | auto | 0.200000 | 0.962733 | 0.988191 |
| 488 | 10.000000 | linear | ovo | 2 | auto | 0.250000 | 0.962779 | 0.988813 |
| 489 | 10.000000 | linear | ovo | 2 | auto | 0.300000 | 0.966874 | 0.988191 |
| 490 | 10.000000 | linear | ovo | 3 | scale | 0.100000 | 0.968944 | 0.984462 |
| 491 | 10.000000 | linear | ovo | 3 | scale | 0.150000 | 0.975207 | 0.986327 |
| 492 | 10.000000 | linear | ovo | 3 | scale | 0.200000 | 0.962733 | 0.988191 |
| 493 | 10.000000 | linear | ovo | 3 | scale | 0.250000 | 0.962779 | 0.988813 |
| 494 | 10.000000 | linear | ovo | 3 | scale | 0.300000 | 0.966874 | 0.988191 |
| 495 | 10.000000 | linear | ovo | 3 | auto | 0.100000 | 0.968944 | 0.984462 |
| 496 | 10.000000 | linear | ovo | 3 | auto | 0.150000 | 0.975207 | 0.986327 |
| 497 | 10.000000 | linear | ovo | 3 | auto | 0.200000 | 0.962733 | 0.988191 |
| 498 | 10.000000 | linear | ovo | 3 | auto | 0.250000 | 0.962779 | 0.988813 |
| 499 | 10.000000 | linear | ovo | 3 | auto | 0.300000 | 0.966874 | 0.988191 |
| 500 | 10.000000 | linear | ovo | 4 | scale | 0.100000 | 0.968944 | 0.984462 |
| 501 | 10.000000 | linear | ovo | 4 | scale | 0.150000 | 0.975207 | 0.986327 |
| 502 | 10.000000 | linear | ovo | 4 | scale | 0.200000 | 0.962733 | 0.988191 |
| 503 | 10.000000 | linear | ovo | 4 | scale | 0.250000 | 0.962779 | 0.988813 |
| 504 | 10.000000 | linear | ovo | 4 | scale | 0.300000 | 0.966874 | 0.988191 |
| 505 | 10.000000 | linear | ovo | 4 | auto | 0.100000 | 0.968944 | 0.984462 |
| 506 | 10.000000 | linear | ovo | 4 | auto | 0.150000 | 0.975207 | 0.986327 |
| 507 | 10.000000 | linear | ovo | 4 | auto | 0.200000 | 0.962733 | 0.988191 |
| 508 | 10.000000 | linear | ovo | 4 | auto | 0.250000 | 0.962779 | 0.988813 |
| 509 | 10.000000 | linear | ovo | 4 | auto | 0.300000 | 0.966874 | 0.988191 |
| 510 | 10.000000 | linear | ovr | 2 | scale | 0.100000 | 0.968944 | 0.984462 |
| 511 | 10.000000 | linear | ovr | 2 | scale | 0.150000 | 0.975207 | 0.986327 |
| 512 | 10.000000 | linear | ovr | 2 | scale | 0.200000 | 0.962733 | 0.988191 |
| 513 | 10.000000 | linear | ovr | 2 | scale | 0.250000 | 0.962779 | 0.988813 |
| 514 | 10.000000 | linear | ovr | 2 | scale | 0.300000 | 0.966874 | 0.988191 |
| 515 | 10.000000 | linear | ovr | 2 | auto | 0.100000 | 0.968944 | 0.984462 |
| 516 | 10.000000 | linear | ovr | 2 | auto | 0.150000 | 0.975207 | 0.986327 |
| 517 | 10.000000 | linear | ovr | 2 | auto | 0.200000 | 0.962733 | 0.988191 |
| 518 | 10.000000 | linear | ovr | 2 | auto | 0.250000 | 0.962779 | 0.988813 |
| 519 | 10.000000 | linear | ovr | 2 | auto | 0.300000 | 0.966874 | 0.988191 |
| 520 | 10.000000 | linear | ovr | 3 | scale | 0.100000 | 0.968944 | 0.984462 |
| 521 | 10.000000 | linear | ovr | 3 | scale | 0.150000 | 0.975207 | 0.986327 |
| 522 | 10.000000 | linear | ovr | 3 | scale | 0.200000 | 0.962733 | 0.988191 |
| 523 | 10.000000 | linear | ovr | 3 | scale | 0.250000 | 0.962779 | 0.988813 |
| 524 | 10.000000 | linear | ovr | 3 | scale | 0.300000 | 0.966874 | 0.988191 |
| 525 | 10.000000 | linear | ovr | 3 | auto | 0.100000 | 0.968944 | 0.984462 |
| 526 | 10.000000 | linear | ovr | 3 | auto | 0.150000 | 0.975207 | 0.986327 |
| 527 | 10.000000 | linear | ovr | 3 | auto | 0.200000 | 0.962733 | 0.988191 |
| 528 | 10.000000 | linear | ovr | 3 | auto | 0.250000 | 0.962779 | 0.988813 |
| 529 | 10.000000 | linear | ovr | 3 | auto | 0.300000 | 0.966874 | 0.988191 |
| 530 | 10.000000 | linear | ovr | 4 | scale | 0.100000 | 0.968944 | 0.984462 |
| 531 | 10.000000 | linear | ovr | 4 | scale | 0.150000 | 0.975207 | 0.986327 |
| 532 | 10.000000 | linear | ovr | 4 | scale | 0.200000 | 0.962733 | 0.988191 |
| 533 | 10.000000 | linear | ovr | 4 | scale | 0.250000 | 0.962779 | 0.988813 |
| 534 | 10.000000 | linear | ovr | 4 | scale | 0.300000 | 0.966874 | 0.988191 |
| 535 | 10.000000 | linear | ovr | 4 | auto | 0.100000 | 0.968944 | 0.984462 |
| 536 | 10.000000 | linear | ovr | 4 | auto | 0.150000 | 0.975207 | 0.986327 |
| 537 | 10.000000 | linear | ovr | 4 | auto | 0.200000 | 0.962733 | 0.988191 |
| 538 | 10.000000 | linear | ovr | 4 | auto | 0.250000 | 0.962779 | 0.988813 |
| 539 | 10.000000 | linear | ovr | 4 | auto | 0.300000 | 0.966874 | 0.988191 |
| 540 | 10.000000 | rbf | ovo | 2 | scale | 0.100000 | 0.962733 | 0.963953 |
| 541 | 10.000000 | rbf | ovo | 2 | scale | 0.150000 | 0.966942 | 0.966439 |
| 542 | 10.000000 | rbf | ovo | 2 | scale | 0.200000 | 0.962733 | 0.967682 |
| 543 | 10.000000 | rbf | ovo | 2 | scale | 0.250000 | 0.952854 | 0.967060 |
| 544 | 10.000000 | rbf | ovo | 2 | scale | 0.300000 | 0.956522 | 0.964574 |
| 545 | 10.000000 | rbf | ovo | 2 | auto | 0.100000 | 0.242236 | 0.924177 |
| 546 | 10.000000 | rbf | ovo | 2 | auto | 0.150000 | 0.252066 | 0.887508 |
| 547 | 10.000000 | rbf | ovo | 2 | auto | 0.200000 | 0.214286 | 0.842759 |
| 548 | 10.000000 | rbf | ovo | 2 | auto | 0.250000 | 0.210918 | 0.802362 |
| 549 | 10.000000 | rbf | ovo | 2 | auto | 0.300000 | 0.229814 | 0.768800 |
| 550 | 10.000000 | rbf | ovo | 3 | scale | 0.100000 | 0.962733 | 0.963953 |
| 551 | 10.000000 | rbf | ovo | 3 | scale | 0.150000 | 0.966942 | 0.966439 |
| 552 | 10.000000 | rbf | ovo | 3 | scale | 0.200000 | 0.962733 | 0.967682 |
| 553 | 10.000000 | rbf | ovo | 3 | scale | 0.250000 | 0.952854 | 0.967060 |
| 554 | 10.000000 | rbf | ovo | 3 | scale | 0.300000 | 0.956522 | 0.964574 |
| 555 | 10.000000 | rbf | ovo | 3 | auto | 0.100000 | 0.242236 | 0.924177 |
| 556 | 10.000000 | rbf | ovo | 3 | auto | 0.150000 | 0.252066 | 0.887508 |
| 557 | 10.000000 | rbf | ovo | 3 | auto | 0.200000 | 0.214286 | 0.842759 |
| 558 | 10.000000 | rbf | ovo | 3 | auto | 0.250000 | 0.210918 | 0.802362 |
| 559 | 10.000000 | rbf | ovo | 3 | auto | 0.300000 | 0.229814 | 0.768800 |
| 560 | 10.000000 | rbf | ovo | 4 | scale | 0.100000 | 0.962733 | 0.963953 |
| 561 | 10.000000 | rbf | ovo | 4 | scale | 0.150000 | 0.966942 | 0.966439 |
| 562 | 10.000000 | rbf | ovo | 4 | scale | 0.200000 | 0.962733 | 0.967682 |
| 563 | 10.000000 | rbf | ovo | 4 | scale | 0.250000 | 0.952854 | 0.967060 |
| 564 | 10.000000 | rbf | ovo | 4 | scale | 0.300000 | 0.956522 | 0.964574 |
| 565 | 10.000000 | rbf | ovo | 4 | auto | 0.100000 | 0.242236 | 0.924177 |
| 566 | 10.000000 | rbf | ovo | 4 | auto | 0.150000 | 0.252066 | 0.887508 |
| 567 | 10.000000 | rbf | ovo | 4 | auto | 0.200000 | 0.214286 | 0.842759 |
| 568 | 10.000000 | rbf | ovo | 4 | auto | 0.250000 | 0.210918 | 0.802362 |
| 569 | 10.000000 | rbf | ovo | 4 | auto | 0.300000 | 0.229814 | 0.768800 |
| 570 | 10.000000 | rbf | ovr | 2 | scale | 0.100000 | 0.962733 | 0.963953 |
| 571 | 10.000000 | rbf | ovr | 2 | scale | 0.150000 | 0.966942 | 0.966439 |
| 572 | 10.000000 | rbf | ovr | 2 | scale | 0.200000 | 0.962733 | 0.967682 |
| 573 | 10.000000 | rbf | ovr | 2 | scale | 0.250000 | 0.952854 | 0.967060 |
| 574 | 10.000000 | rbf | ovr | 2 | scale | 0.300000 | 0.956522 | 0.964574 |
| 575 | 10.000000 | rbf | ovr | 2 | auto | 0.100000 | 0.242236 | 0.924177 |
| 576 | 10.000000 | rbf | ovr | 2 | auto | 0.150000 | 0.252066 | 0.887508 |
| 577 | 10.000000 | rbf | ovr | 2 | auto | 0.200000 | 0.214286 | 0.842759 |
| 578 | 10.000000 | rbf | ovr | 2 | auto | 0.250000 | 0.210918 | 0.802362 |
| 579 | 10.000000 | rbf | ovr | 2 | auto | 0.300000 | 0.229814 | 0.768800 |
| 580 | 10.000000 | rbf | ovr | 3 | scale | 0.100000 | 0.962733 | 0.963953 |
| 581 | 10.000000 | rbf | ovr | 3 | scale | 0.150000 | 0.966942 | 0.966439 |
| 582 | 10.000000 | rbf | ovr | 3 | scale | 0.200000 | 0.962733 | 0.967682 |
| 583 | 10.000000 | rbf | ovr | 3 | scale | 0.250000 | 0.952854 | 0.967060 |
| 584 | 10.000000 | rbf | ovr | 3 | scale | 0.300000 | 0.956522 | 0.964574 |
| 585 | 10.000000 | rbf | ovr | 3 | auto | 0.100000 | 0.242236 | 0.924177 |
| 586 | 10.000000 | rbf | ovr | 3 | auto | 0.150000 | 0.252066 | 0.887508 |
| 587 | 10.000000 | rbf | ovr | 3 | auto | 0.200000 | 0.214286 | 0.842759 |
| 588 | 10.000000 | rbf | ovr | 3 | auto | 0.250000 | 0.210918 | 0.802362 |
| 589 | 10.000000 | rbf | ovr | 3 | auto | 0.300000 | 0.229814 | 0.768800 |
| 590 | 10.000000 | rbf | ovr | 4 | scale | 0.100000 | 0.962733 | 0.963953 |
| 591 | 10.000000 | rbf | ovr | 4 | scale | 0.150000 | 0.966942 | 0.966439 |
| 592 | 10.000000 | rbf | ovr | 4 | scale | 0.200000 | 0.962733 | 0.967682 |
| 593 | 10.000000 | rbf | ovr | 4 | scale | 0.250000 | 0.952854 | 0.967060 |
| 594 | 10.000000 | rbf | ovr | 4 | scale | 0.300000 | 0.956522 | 0.964574 |
| 595 | 10.000000 | rbf | ovr | 4 | auto | 0.100000 | 0.242236 | 0.924177 |
| 596 | 10.000000 | rbf | ovr | 4 | auto | 0.150000 | 0.252066 | 0.887508 |
| 597 | 10.000000 | rbf | ovr | 4 | auto | 0.200000 | 0.214286 | 0.842759 |
| 598 | 10.000000 | rbf | ovr | 4 | auto | 0.250000 | 0.210918 | 0.802362 |
| 599 | 10.000000 | rbf | ovr | 4 | auto | 0.300000 | 0.229814 | 0.768800 |
| 600 | 10.000000 | poly | ovo | 2 | scale | 0.100000 | 0.968944 | 0.965817 |
| 601 | 10.000000 | poly | ovo | 2 | scale | 0.150000 | 0.971074 | 0.964574 |
| 602 | 10.000000 | poly | ovo | 2 | scale | 0.200000 | 0.962733 | 0.966439 |
| 603 | 10.000000 | poly | ovo | 2 | scale | 0.250000 | 0.955335 | 0.965196 |
| 604 | 10.000000 | poly | ovo | 2 | scale | 0.300000 | 0.962733 | 0.968925 |
| 605 | 10.000000 | poly | ovo | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 606 | 10.000000 | poly | ovo | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 607 | 10.000000 | poly | ovo | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 608 | 10.000000 | poly | ovo | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 609 | 10.000000 | poly | ovo | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 610 | 10.000000 | poly | ovo | 3 | scale | 0.100000 | 0.968944 | 0.971411 |
| 611 | 10.000000 | poly | ovo | 3 | scale | 0.150000 | 0.971074 | 0.970168 |
| 612 | 10.000000 | poly | ovo | 3 | scale | 0.200000 | 0.962733 | 0.968925 |
| 613 | 10.000000 | poly | ovo | 3 | scale | 0.250000 | 0.955335 | 0.969546 |
| 614 | 10.000000 | poly | ovo | 3 | scale | 0.300000 | 0.962733 | 0.969546 |
| 615 | 10.000000 | poly | ovo | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 616 | 10.000000 | poly | ovo | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 617 | 10.000000 | poly | ovo | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 618 | 10.000000 | poly | ovo | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 619 | 10.000000 | poly | ovo | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 620 | 10.000000 | poly | ovo | 4 | scale | 0.100000 | 0.962733 | 0.974518 |
| 621 | 10.000000 | poly | ovo | 4 | scale | 0.150000 | 0.966942 | 0.971411 |
| 622 | 10.000000 | poly | ovo | 4 | scale | 0.200000 | 0.965839 | 0.972654 |
| 623 | 10.000000 | poly | ovo | 4 | scale | 0.250000 | 0.957816 | 0.970789 |
| 624 | 10.000000 | poly | ovo | 4 | scale | 0.300000 | 0.962733 | 0.970168 |
| 625 | 10.000000 | poly | ovo | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 626 | 10.000000 | poly | ovo | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 627 | 10.000000 | poly | ovo | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 628 | 10.000000 | poly | ovo | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 629 | 10.000000 | poly | ovo | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 630 | 10.000000 | poly | ovr | 2 | scale | 0.100000 | 0.968944 | 0.965817 |
| 631 | 10.000000 | poly | ovr | 2 | scale | 0.150000 | 0.971074 | 0.964574 |
| 632 | 10.000000 | poly | ovr | 2 | scale | 0.200000 | 0.962733 | 0.966439 |
| 633 | 10.000000 | poly | ovr | 2 | scale | 0.250000 | 0.955335 | 0.965196 |
| 634 | 10.000000 | poly | ovr | 2 | scale | 0.300000 | 0.962733 | 0.968925 |
| 635 | 10.000000 | poly | ovr | 2 | auto | 0.100000 | 0.975155 | 0.997514 |
| 636 | 10.000000 | poly | ovr | 2 | auto | 0.150000 | 0.975207 | 0.996271 |
| 637 | 10.000000 | poly | ovr | 2 | auto | 0.200000 | 0.968944 | 0.993785 |
| 638 | 10.000000 | poly | ovr | 2 | auto | 0.250000 | 0.960298 | 0.990056 |
| 639 | 10.000000 | poly | ovr | 2 | auto | 0.300000 | 0.960663 | 0.988191 |
| 640 | 10.000000 | poly | ovr | 3 | scale | 0.100000 | 0.968944 | 0.971411 |
| 641 | 10.000000 | poly | ovr | 3 | scale | 0.150000 | 0.971074 | 0.970168 |
| 642 | 10.000000 | poly | ovr | 3 | scale | 0.200000 | 0.962733 | 0.968925 |
| 643 | 10.000000 | poly | ovr | 3 | scale | 0.250000 | 0.955335 | 0.969546 |
| 644 | 10.000000 | poly | ovr | 3 | scale | 0.300000 | 0.962733 | 0.969546 |
| 645 | 10.000000 | poly | ovr | 3 | auto | 0.100000 | 0.968944 | 0.996892 |
| 646 | 10.000000 | poly | ovr | 3 | auto | 0.150000 | 0.975207 | 0.996271 |
| 647 | 10.000000 | poly | ovr | 3 | auto | 0.200000 | 0.962733 | 0.992542 |
| 648 | 10.000000 | poly | ovr | 3 | auto | 0.250000 | 0.957816 | 0.989434 |
| 649 | 10.000000 | poly | ovr | 3 | auto | 0.300000 | 0.960663 | 0.988191 |
| 650 | 10.000000 | poly | ovr | 4 | scale | 0.100000 | 0.962733 | 0.974518 |
| 651 | 10.000000 | poly | ovr | 4 | scale | 0.150000 | 0.966942 | 0.971411 |
| 652 | 10.000000 | poly | ovr | 4 | scale | 0.200000 | 0.965839 | 0.972654 |
| 653 | 10.000000 | poly | ovr | 4 | scale | 0.250000 | 0.957816 | 0.970789 |
| 654 | 10.000000 | poly | ovr | 4 | scale | 0.300000 | 0.962733 | 0.970168 |
| 655 | 10.000000 | poly | ovr | 4 | auto | 0.100000 | 0.968944 | 0.996892 |
| 656 | 10.000000 | poly | ovr | 4 | auto | 0.150000 | 0.971074 | 0.995649 |
| 657 | 10.000000 | poly | ovr | 4 | auto | 0.200000 | 0.962733 | 0.992542 |
| 658 | 10.000000 | poly | ovr | 4 | auto | 0.250000 | 0.960298 | 0.990056 |
| 659 | 10.000000 | poly | ovr | 4 | auto | 0.300000 | 0.958592 | 0.987570 |
| 660 | 10.000000 | sigmoid | ovo | 2 | scale | 0.100000 | 0.186335 | 0.174021 |
| 661 | 10.000000 | sigmoid | ovo | 2 | scale | 0.150000 | 0.181818 | 0.178372 |
| 662 | 10.000000 | sigmoid | ovo | 2 | scale | 0.200000 | 0.180124 | 0.185208 |
| 663 | 10.000000 | sigmoid | ovo | 2 | scale | 0.250000 | 0.183623 | 0.188316 |
| 664 | 10.000000 | sigmoid | ovo | 2 | scale | 0.300000 | 0.184265 | 0.185830 |
| 665 | 10.000000 | sigmoid | ovo | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 666 | 10.000000 | sigmoid | ovo | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 667 | 10.000000 | sigmoid | ovo | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 668 | 10.000000 | sigmoid | ovo | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 669 | 10.000000 | sigmoid | ovo | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 670 | 10.000000 | sigmoid | ovo | 3 | scale | 0.100000 | 0.186335 | 0.174021 |
| 671 | 10.000000 | sigmoid | ovo | 3 | scale | 0.150000 | 0.181818 | 0.178372 |
| 672 | 10.000000 | sigmoid | ovo | 3 | scale | 0.200000 | 0.180124 | 0.185208 |
| 673 | 10.000000 | sigmoid | ovo | 3 | scale | 0.250000 | 0.183623 | 0.188316 |
| 674 | 10.000000 | sigmoid | ovo | 3 | scale | 0.300000 | 0.184265 | 0.185830 |
| 675 | 10.000000 | sigmoid | ovo | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 676 | 10.000000 | sigmoid | ovo | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 677 | 10.000000 | sigmoid | ovo | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 678 | 10.000000 | sigmoid | ovo | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 679 | 10.000000 | sigmoid | ovo | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 680 | 10.000000 | sigmoid | ovo | 4 | scale | 0.100000 | 0.186335 | 0.174021 |
| 681 | 10.000000 | sigmoid | ovo | 4 | scale | 0.150000 | 0.181818 | 0.178372 |
| 682 | 10.000000 | sigmoid | ovo | 4 | scale | 0.200000 | 0.180124 | 0.185208 |
| 683 | 10.000000 | sigmoid | ovo | 4 | scale | 0.250000 | 0.183623 | 0.188316 |
| 684 | 10.000000 | sigmoid | ovo | 4 | scale | 0.300000 | 0.184265 | 0.185830 |
| 685 | 10.000000 | sigmoid | ovo | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 686 | 10.000000 | sigmoid | ovo | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 687 | 10.000000 | sigmoid | ovo | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 688 | 10.000000 | sigmoid | ovo | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 689 | 10.000000 | sigmoid | ovo | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
| 690 | 10.000000 | sigmoid | ovr | 2 | scale | 0.100000 | 0.186335 | 0.174021 |
| 691 | 10.000000 | sigmoid | ovr | 2 | scale | 0.150000 | 0.181818 | 0.178372 |
| 692 | 10.000000 | sigmoid | ovr | 2 | scale | 0.200000 | 0.180124 | 0.185208 |
| 693 | 10.000000 | sigmoid | ovr | 2 | scale | 0.250000 | 0.183623 | 0.188316 |
| 694 | 10.000000 | sigmoid | ovr | 2 | scale | 0.300000 | 0.184265 | 0.185830 |
| 695 | 10.000000 | sigmoid | ovr | 2 | auto | 0.100000 | 0.242236 | 0.253574 |
| 696 | 10.000000 | sigmoid | ovr | 2 | auto | 0.150000 | 0.252066 | 0.253574 |
| 697 | 10.000000 | sigmoid | ovr | 2 | auto | 0.200000 | 0.214286 | 0.249223 |
| 698 | 10.000000 | sigmoid | ovr | 2 | auto | 0.250000 | 0.210918 | 0.249223 |
| 699 | 10.000000 | sigmoid | ovr | 2 | auto | 0.300000 | 0.229814 | 0.249223 |
| 700 | 10.000000 | sigmoid | ovr | 3 | scale | 0.100000 | 0.186335 | 0.174021 |
| 701 | 10.000000 | sigmoid | ovr | 3 | scale | 0.150000 | 0.181818 | 0.178372 |
| 702 | 10.000000 | sigmoid | ovr | 3 | scale | 0.200000 | 0.180124 | 0.185208 |
| 703 | 10.000000 | sigmoid | ovr | 3 | scale | 0.250000 | 0.183623 | 0.188316 |
| 704 | 10.000000 | sigmoid | ovr | 3 | scale | 0.300000 | 0.184265 | 0.185830 |
| 705 | 10.000000 | sigmoid | ovr | 3 | auto | 0.100000 | 0.242236 | 0.253574 |
| 706 | 10.000000 | sigmoid | ovr | 3 | auto | 0.150000 | 0.252066 | 0.253574 |
| 707 | 10.000000 | sigmoid | ovr | 3 | auto | 0.200000 | 0.214286 | 0.249223 |
| 708 | 10.000000 | sigmoid | ovr | 3 | auto | 0.250000 | 0.210918 | 0.249223 |
| 709 | 10.000000 | sigmoid | ovr | 3 | auto | 0.300000 | 0.229814 | 0.249223 |
| 710 | 10.000000 | sigmoid | ovr | 4 | scale | 0.100000 | 0.186335 | 0.174021 |
| 711 | 10.000000 | sigmoid | ovr | 4 | scale | 0.150000 | 0.181818 | 0.178372 |
| 712 | 10.000000 | sigmoid | ovr | 4 | scale | 0.200000 | 0.180124 | 0.185208 |
| 713 | 10.000000 | sigmoid | ovr | 4 | scale | 0.250000 | 0.183623 | 0.188316 |
| 714 | 10.000000 | sigmoid | ovr | 4 | scale | 0.300000 | 0.184265 | 0.185830 |
| 715 | 10.000000 | sigmoid | ovr | 4 | auto | 0.100000 | 0.242236 | 0.253574 |
| 716 | 10.000000 | sigmoid | ovr | 4 | auto | 0.150000 | 0.252066 | 0.253574 |
| 717 | 10.000000 | sigmoid | ovr | 4 | auto | 0.200000 | 0.214286 | 0.249223 |
| 718 | 10.000000 | sigmoid | ovr | 4 | auto | 0.250000 | 0.210918 | 0.249223 |
| 719 | 10.000000 | sigmoid | ovr | 4 | auto | 0.300000 | 0.229814 | 0.249223 |
Row=1, C=0.1, kernel=linear, decision_function_shape=ovo, degree=2, gamma=scale, Test_size=0.15, acc=0.983471, score=0.988813
In [54]:
X_train, X_test, y_train, y_test = train_test_split(Xs, ys, test_size=0.15, random_state=0)
In [55]:
clf_svm = SVC(C=0.1, kernel='linear', decision_function_shape='ovo',degree=2, gamma='scale')
clf_svm.fit(X_train, y_train)
y_pred = clf_svm.predict(X_test)
In [56]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_svm.score(Xs, ys))
Accuracy = 0.9834710743801653 Score = 0.9888129272840274
Best model =>SVM¶
Predict Test data¶
In [57]:
df_test
Out[57]:
| id | battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1043 | 1 | 1.8 | 1 | 14 | 0 | 5 | 0.1 | 193 | 3 | 16 | 226 | 1412 | 3476 | 12 | 7 | 2 | 0 | 1 | 0 |
| 1 | 3 | 1807 | 1 | 2.8 | 0 | 1 | 0 | 27 | 0.9 | 186 | 3 | 4 | 1270 | 1366 | 2396 | 17 | 10 | 10 | 0 | 1 | 1 |
| 2 | 5 | 1434 | 0 | 1.4 | 0 | 11 | 1 | 49 | 0.5 | 108 | 6 | 18 | 749 | 810 | 1773 | 15 | 8 | 7 | 1 | 0 | 1 |
| 3 | 6 | 1464 | 1 | 2.9 | 1 | 5 | 1 | 50 | 0.8 | 198 | 8 | 9 | 569 | 939 | 3506 | 10 | 7 | 3 | 1 | 1 | 1 |
| 4 | 7 | 1718 | 0 | 2.4 | 0 | 1 | 0 | 47 | 1.0 | 156 | 2 | 3 | 1283 | 1374 | 3873 | 14 | 2 | 10 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 782 | 994 | 567 | 1 | 2.7 | 1 | 14 | 1 | 56 | 0.4 | 165 | 8 | 17 | 555 | 1290 | 336 | 7 | 6 | 7 | 1 | 1 | 1 |
| 783 | 995 | 936 | 1 | 1.4 | 1 | 0 | 0 | 46 | 0.8 | 139 | 2 | 0 | 265 | 886 | 684 | 8 | 5 | 12 | 1 | 1 | 1 |
| 784 | 996 | 1700 | 1 | 1.9 | 0 | 0 | 1 | 54 | 0.5 | 170 | 7 | 17 | 644 | 913 | 2121 | 14 | 8 | 15 | 1 | 1 | 0 |
| 785 | 999 | 1533 | 1 | 0.5 | 1 | 0 | 0 | 50 | 0.4 | 171 | 2 | 12 | 38 | 832 | 2509 | 15 | 11 | 6 | 0 | 1 | 0 |
| 786 | 1000 | 1270 | 1 | 0.5 | 0 | 4 | 1 | 35 | 0.1 | 140 | 6 | 19 | 457 | 608 | 2828 | 9 | 2 | 3 | 1 | 0 | 1 |
787 rows × 21 columns
In [58]:
df_test = df_test.drop('id', axis=1)
In [59]:
predict = clf_svm.predict(df_test)
In [60]:
predict
Out[60]:
array([3, 2, 1, 3, 3, 1, 3, 3, 3, 0, 2, 0, 2, 1, 3, 3, 1, 3, 0, 2, 0, 3,
0, 2, 0, 3, 0, 0, 1, 3, 1, 1, 1, 2, 0, 0, 1, 3, 1, 1, 0, 0, 3, 1,
3, 1, 3, 3, 1, 2, 1, 2, 1, 2, 2, 3, 0, 0, 2, 0, 3, 3, 0, 3, 0, 3,
1, 3, 1, 2, 2, 1, 2, 2, 0, 0, 3, 0, 2, 0, 1, 2, 3, 3, 2, 3, 3, 3,
2, 3, 0, 0, 3, 2, 1, 2, 0, 2, 3, 2, 2, 1, 1, 3, 1, 1, 1, 1, 2, 3,
3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 3, 1, 3, 0, 2, 0, 1, 0,
0, 1, 2, 1, 0, 0, 1, 2, 2, 1, 0, 0, 1, 0, 1, 0, 2, 3, 3, 2, 3, 2,
3, 2, 1, 1, 0, 1, 2, 0, 2, 3, 0, 2, 0, 3, 2, 3, 0, 1, 0, 3, 0, 0,
2, 2, 1, 3, 3, 0, 3, 1, 2, 0, 0, 1, 3, 3, 3, 0, 0, 2, 3, 1, 3, 1,
3, 1, 2, 3, 3, 1, 0, 1, 3, 1, 1, 3, 2, 1, 0, 1, 2, 1, 0, 3, 2, 3,
3, 2, 3, 3, 2, 1, 1, 0, 2, 0, 0, 3, 0, 3, 0, 1, 2, 0, 2, 3, 1, 2,
2, 1, 0, 0, 1, 3, 2, 0, 0, 0, 3, 0, 2, 3, 1, 2, 2, 2, 1, 3, 3, 2,
2, 3, 3, 3, 3, 3, 1, 2, 3, 0, 1, 0, 3, 1, 2, 3, 0, 0, 0, 0, 2, 0,
2, 2, 2, 2, 0, 0, 0, 3, 0, 3, 2, 2, 1, 2, 3, 1, 1, 2, 0, 1, 0, 3,
2, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 3, 2, 3, 0, 3, 0, 1, 0, 2, 0, 3,
2, 3, 3, 1, 3, 1, 3, 3, 2, 0, 1, 2, 1, 0, 0, 1, 2, 1, 0, 3, 2, 0,
2, 2, 0, 0, 3, 1, 0, 2, 3, 3, 0, 3, 2, 3, 2, 3, 0, 2, 0, 2, 0, 1,
0, 1, 1, 1, 3, 3, 3, 2, 1, 2, 2, 3, 3, 3, 2, 2, 1, 2, 2, 1, 0, 2,
2, 0, 0, 0, 3, 1, 0, 2, 2, 2, 0, 3, 2, 2, 1, 0, 3, 0, 1, 3, 1, 1,
2, 2, 0, 2, 1, 2, 3, 1, 2, 2, 3, 2, 3, 1, 3, 2, 1, 1, 0, 0, 3, 1,
0, 3, 3, 2, 1, 3, 3, 2, 3, 3, 2, 0, 1, 1, 2, 2, 2, 0, 0, 2, 2, 2,
2, 1, 3, 3, 0, 1, 3, 2, 1, 1, 0, 0, 2, 1, 0, 1, 2, 0, 2, 2, 1, 0,
3, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 3, 1, 3, 2, 1, 3, 0, 1, 1, 3, 2,
2, 0, 3, 0, 2, 0, 0, 1, 1, 2, 1, 1, 3, 2, 1, 3, 0, 2, 2, 3, 3, 0,
2, 1, 2, 0, 3, 0, 3, 3, 3, 0, 2, 2, 3, 2, 2, 1, 2, 3, 0, 1, 0, 1,
2, 0, 1, 0, 0, 3, 0, 2, 0, 1, 1, 0, 3, 2, 0, 0, 1, 2, 2, 1, 0, 1,
2, 0, 1, 1, 0, 0, 3, 0, 3, 1, 3, 0, 1, 0, 2, 1, 0, 1, 1, 3, 2, 0,
3, 2, 0, 0, 0, 3, 3, 2, 0, 2, 1, 3, 0, 2, 0, 3, 1, 2, 1, 1, 3, 1,
1, 2, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 2, 3, 0, 1, 1, 0, 1, 0, 2, 0,
3, 2, 2, 1, 2, 0, 3, 3, 2, 3, 0, 2, 3, 3, 3, 2, 1, 0, 0, 3, 3, 1,
0, 0, 0, 2, 2, 3, 2, 1, 2, 3, 3, 0, 1, 2, 1, 2, 2, 3, 1, 3, 0, 2,
3, 2, 1, 1, 1, 3, 0, 2, 2, 3, 2, 2, 3, 2, 0, 1, 2, 1, 2, 2, 2, 1,
2, 1, 1, 3, 1, 0, 1, 2, 1, 0, 3, 2, 3, 0, 3, 2, 1, 3, 0, 3, 1, 1,
1, 3, 2, 0, 3, 0, 3, 0, 3, 3, 1, 0, 2, 3, 1, 0, 2, 2, 1, 2, 0, 2,
2, 0, 2, 2, 3, 0, 2, 1, 1, 2, 2, 3, 3, 0, 2, 1, 2, 3, 1, 1, 3, 0,
1, 0, 0, 3, 3, 2, 0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2], dtype=int64)
In [61]:
df_test['price_range'] = predict
In [62]:
df_test
Out[62]:
| battery_power | blue | clock_speed | dual_sim | fc | four_g | int_memory | m_dep | mobile_wt | n_cores | pc | px_height | px_width | ram | sc_h | sc_w | talk_time | three_g | touch_screen | wifi | price_range | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1043 | 1 | 1.8 | 1 | 14 | 0 | 5 | 0.1 | 193 | 3 | 16 | 226 | 1412 | 3476 | 12 | 7 | 2 | 0 | 1 | 0 | 3 |
| 1 | 1807 | 1 | 2.8 | 0 | 1 | 0 | 27 | 0.9 | 186 | 3 | 4 | 1270 | 1366 | 2396 | 17 | 10 | 10 | 0 | 1 | 1 | 2 |
| 2 | 1434 | 0 | 1.4 | 0 | 11 | 1 | 49 | 0.5 | 108 | 6 | 18 | 749 | 810 | 1773 | 15 | 8 | 7 | 1 | 0 | 1 | 1 |
| 3 | 1464 | 1 | 2.9 | 1 | 5 | 1 | 50 | 0.8 | 198 | 8 | 9 | 569 | 939 | 3506 | 10 | 7 | 3 | 1 | 1 | 1 | 3 |
| 4 | 1718 | 0 | 2.4 | 0 | 1 | 0 | 47 | 1.0 | 156 | 2 | 3 | 1283 | 1374 | 3873 | 14 | 2 | 10 | 0 | 0 | 0 | 3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 782 | 567 | 1 | 2.7 | 1 | 14 | 1 | 56 | 0.4 | 165 | 8 | 17 | 555 | 1290 | 336 | 7 | 6 | 7 | 1 | 1 | 1 | 0 |
| 783 | 936 | 1 | 1.4 | 1 | 0 | 0 | 46 | 0.8 | 139 | 2 | 0 | 265 | 886 | 684 | 8 | 5 | 12 | 1 | 1 | 1 | 0 |
| 784 | 1700 | 1 | 1.9 | 0 | 0 | 1 | 54 | 0.5 | 170 | 7 | 17 | 644 | 913 | 2121 | 14 | 8 | 15 | 1 | 1 | 0 | 2 |
| 785 | 1533 | 1 | 0.5 | 1 | 0 | 0 | 50 | 0.4 | 171 | 2 | 12 | 38 | 832 | 2509 | 15 | 11 | 6 | 0 | 1 | 0 | 2 |
| 786 | 1270 | 1 | 0.5 | 0 | 4 | 1 | 35 | 0.1 | 140 | 6 | 19 | 457 | 608 | 2828 | 9 | 2 | 3 | 1 | 0 | 1 | 2 |
787 rows × 21 columns
In [63]:
labels = ['0', '1', '2', '3']
values = df_test['price_range'].value_counts().values
fig = px.pie(values=values,
names=labels,
hole=0.3)
fig.update_layout(
title_text="Price range of test dataset",
title_x=0.5,
title_yanchor="middle"
)
fig.show()